An attention based model for image caption generation.
Implementation of an extension of the paper
import glob #for reading images
import matplotlib.pyplot as plt #for plotting graphs
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.image as mpimg
from skimage import io
pd.set_option('display.max_colwidth',None) # Set the max column width to see the complete caption
import collections #for counter
from wordcloud import WordCloud,STOPWORDS
import keras
from keras.preprocessing.image import load_img
import string
import time
from sklearn.model_selection import train_test_split
import tensorflow as tf
from keras.preprocessing.text import Tokenizer
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.models import Model
from tensorflow.keras import layers
from tensorflow.keras import activations
from tensorflow.keras import Input
from tqdm import tqdm
from PIL import Image
from gtts import gTTS
from playsound import playsound
from IPython import display
from nltk.translate.bleu_score import sentence_bleu
import more_itertools
#reading all images in Flickr8K dataset
imageFolder = 'Flickr8K/Images'
images = glob.glob(imageFolder + '/*.jpg', recursive = True)
print("No of datasets:", len(images))
No of datasets: 8091
#some images
displayImages= images[0:5]
figure, axes = plt.subplots(1,5)
figure.set_figwidth(20)
for ax,image in zip(axes, displayImages):
ax.imshow(io.imread(image), cmap=None)
displayImages= images[5:10]
figure, axes = plt.subplots(1,5)
figure.set_figwidth(20)
for ax,image in zip(axes, displayImages):
ax.imshow(io.imread(image), cmap=None)
def load_doc(text_file):
open_file = open(text_file, 'r', encoding="latin-1")
text = open_file.read()
print(text)
open_file.close()
return text
#read captions
textFile = 'Flickr8K/captions.txt'
doc = load_doc(textFile)
print(doc[:300])
IOPub data rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_data_rate_limit`. Current values: NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec) NotebookApp.rate_limit_window=3.0 (secs)
#creating a dataframe with all info: imageNames, imageLocations, imageCaptions
imagePath = 'Flickr8K/Images/'
imageNames = []
imageLocations = []
imageCaptions = []
with open('Flickr8K/captions.txt', 'r') as fo:
next(fo) # to skip the heading
for line in fo:
splitDetails=line.split(',')
imageNames.append(splitDetails[0])
imageCaptions.append(splitDetails[1].rstrip('\n.')) #striping \n and fullstop
imageLocations.append(imagePath+splitDetails[0])
df = pd.DataFrame(list(zip(imageNames, imageLocations, imageCaptions)), columns = ['ID', 'Path', 'Captions'])
df
| ID | Path | Captions | |
|---|---|---|---|
| 0 | 1000268201_693b08cb0e.jpg | Flickr8K/Images/1000268201_693b08cb0e.jpg | A child in a pink dress is climbing up a set of stairs in an entry way |
| 1 | 1000268201_693b08cb0e.jpg | Flickr8K/Images/1000268201_693b08cb0e.jpg | A girl going into a wooden building |
| 2 | 1000268201_693b08cb0e.jpg | Flickr8K/Images/1000268201_693b08cb0e.jpg | A little girl climbing into a wooden playhouse |
| 3 | 1000268201_693b08cb0e.jpg | Flickr8K/Images/1000268201_693b08cb0e.jpg | A little girl climbing the stairs to her playhouse |
| 4 | 1000268201_693b08cb0e.jpg | Flickr8K/Images/1000268201_693b08cb0e.jpg | A little girl in a pink dress going into a wooden cabin |
| ... | ... | ... | ... |
| 40450 | 997722733_0cb5439472.jpg | Flickr8K/Images/997722733_0cb5439472.jpg | A man in a pink shirt climbs a rock face |
| 40451 | 997722733_0cb5439472.jpg | Flickr8K/Images/997722733_0cb5439472.jpg | A man is rock climbing high in the air |
| 40452 | 997722733_0cb5439472.jpg | Flickr8K/Images/997722733_0cb5439472.jpg | A person in a red shirt climbing up a rock face covered in assist handles |
| 40453 | 997722733_0cb5439472.jpg | Flickr8K/Images/997722733_0cb5439472.jpg | A rock climber in a red shirt |
| 40454 | 997722733_0cb5439472.jpg | Flickr8K/Images/997722733_0cb5439472.jpg | A rock climber practices on a rock climbing wall |
40455 rows × 3 columns
#Some details about the dataset
print("No of captions (5*no of images) =", len(imageCaptions))
No of captions (5*no of images) = 40455
#getting all the different words (these will be used in the resultant captions as well)
words = [word for line in imageCaptions for word in line.split()]
print("Words are:")
words
Words are:
['A', 'child', 'in', 'a', 'pink', 'dress', 'is', 'climbing', 'up', 'a', 'set', 'of', 'stairs', 'in', 'an', 'entry', 'way', 'A', 'girl', 'going', 'into', 'a', 'wooden', 'building', 'A', 'little', 'girl', 'climbing', 'into', 'a', 'wooden', 'playhouse', 'A', 'little', 'girl', 'climbing', 'the', 'stairs', 'to', 'her', 'playhouse', 'A', 'little', 'girl', 'in', 'a', 'pink', 'dress', 'going', 'into', 'a', 'wooden', 'cabin', 'A', 'black', 'dog', 'and', 'a', 'spotted', 'dog', 'are', 'fighting', 'A', 'black', 'dog', 'and', 'a', 'tri-colored', 'dog', 'playing', 'with', 'each', 'other', 'on', 'the', 'road', 'A', 'black', 'dog', 'and', 'a', 'white', 'dog', 'with', 'brown', 'spots', 'are', 'staring', 'at', 'each', 'other', 'in', 'the', 'street', 'Two', 'dogs', 'of', 'different', 'breeds', 'looking', 'at', 'each', 'other', 'on', 'the', 'road', 'Two', 'dogs', 'on', 'pavement', 'moving', 'toward', 'each', 'other', 'A', 'little', 'girl', 'covered', 'in', 'paint', 'sits', 'in', 'front', 'of', 'a', 'painted', 'rainbow', 'with', 'her', 'hands', 'in', 'a', 'bowl', 'A', 'little', 'girl', 'is', 'sitting', 'in', 'front', 'of', 'a', 'large', 'painted', 'rainbow', 'A', 'small', 'girl', 'in', 'the', 'grass', 'plays', 'with', 'fingerpaints', 'in', 'front', 'of', 'a', 'white', 'canvas', 'with', 'a', 'rainbow', 'on', 'it', 'There', 'is', 'a', 'girl', 'with', 'pigtails', 'sitting', 'in', 'front', 'of', 'a', 'rainbow', 'painting', 'Young', 'girl', 'with', 'pigtails', 'painting', 'outside', 'in', 'the', 'grass', 'A', 'man', 'lays', 'on', 'a', 'bench', 'while', 'his', 'dog', 'sits', 'by', 'him', 'A', 'man', 'lays', 'on', 'the', 'bench', 'to', 'which', 'a', 'white', 'dog', 'is', 'also', 'tied', 'a', 'man', 'sleeping', 'on', 'a', 'bench', 'outside', 'with', 'a', 'white', 'and', 'black', 'dog', 'sitting', 'next', 'to', 'him', 'A', 'shirtless', 'man', 'lies', 'on', 'a', 'park', 'bench', 'with', 'his', 'dog', 'man', 'laying', 'on', 'bench', 'holding', 'leash', 'of', 'dog', 'sitting', 'on', 'ground', 'A', 'man', 'in', 'an', 'orange', 'hat', 'starring', 'at', 'something', 'A', 'man', 'wears', 'an', 'orange', 'hat', 'and', 'glasses', 'A', 'man', 'with', 'gauges', 'and', 'glasses', 'is', 'wearing', 'a', 'Blitz', 'hat', 'A', 'man', 'with', 'glasses', 'is', 'wearing', 'a', 'beer', 'can', 'crocheted', 'hat', 'The', 'man', 'with', 'pierced', 'ears', 'is', 'wearing', 'glasses', 'and', 'an', 'orange', 'hat', 'A', 'child', 'playing', 'on', 'a', 'rope', 'net', 'A', 'little', 'girl', 'climbing', 'on', 'red', 'roping', 'A', 'little', 'girl', 'in', 'pink', 'climbs', 'a', 'rope', 'bridge', 'at', 'the', 'park', 'A', 'small', 'child', 'grips', 'onto', 'the', 'red', 'ropes', 'at', 'the', 'playground', 'The', 'small', 'child', 'climbs', 'on', 'a', 'red', 'ropes', 'on', 'a', 'playground', 'A', 'black', 'and', 'white', 'dog', 'is', 'running', 'in', 'a', 'grassy', 'garden', 'surrounded', 'by', 'a', 'white', 'fence', 'A', 'black', 'and', 'white', 'dog', 'is', 'running', 'through', 'the', 'grass', 'A', 'Boston', 'terrier', 'is', 'running', 'in', 'the', 'grass', 'A', 'Boston', 'Terrier', 'is', 'running', 'on', 'lush', 'green', 'grass', 'in', 'front', 'of', 'a', 'white', 'fence', 'A', 'dog', 'runs', 'on', 'the', 'green', 'grass', 'near', 'a', 'wooden', 'fence', '"A', 'dog', 'shakes', 'its', 'head', 'near', 'the', 'shore', 'A', 'white', 'dog', 'shakes', 'on', 'the', 'edge', 'of', 'a', 'beach', 'with', 'an', 'orange', 'ball', '"Dog', 'with', 'orange', 'ball', 'at', 'feet', 'White', 'dog', 'playing', 'with', 'a', 'red', 'ball', 'on', 'the', 'shore', 'near', 'the', 'water', 'White', 'dog', 'with', 'brown', 'ears', 'standing', 'near', 'water', 'with', 'head', 'turned', 'to', 'one', 'side', 'A', 'boy', 'smiles', 'in', 'front', 'of', 'a', 'stony', 'wall', 'in', 'a', 'city', 'A', 'little', 'boy', 'is', 'standing', 'on', 'the', 'street', 'while', 'a', 'man', 'in', 'overalls', 'is', 'working', 'on', 'a', 'stone', 'wall', 'A', 'young', 'boy', 'runs', 'aross', 'the', 'street', 'A', 'young', 'child', 'is', 'walking', 'on', 'a', 'stone', 'paved', 'street', 'with', 'a', 'metal', 'pole', 'and', 'a', 'man', 'behind', 'him', 'Smiling', 'boy', 'in', 'white', 'shirt', 'and', 'blue', 'jeans', 'in', 'front', 'of', 'rock', 'wall', 'with', 'man', 'in', 'overalls', 'behind', 'him', 'A', 'black', 'dog', 'leaps', 'over', 'a', 'log', 'A', 'grey', 'dog', 'is', 'leaping', 'over', 'a', 'fallen', 'tree', 'A', 'large', 'black', 'dog', 'leaps', 'a', 'fallen', 'log', 'A', 'mottled', 'black', 'and', 'grey', 'dog', 'in', 'a', 'blue', 'collar', 'jumping', 'over', 'a', 'fallen', 'tree', 'The', 'black', 'dog', 'jumped', 'the', 'tree', 'stump', 'A', 'brown', 'and', 'white', 'dog', 'is', 'running', 'through', 'the', 'snow', 'A', 'dog', 'is', 'running', 'in', 'the', 'snow', 'A', 'dog', 'running', 'through', 'snow', 'a', 'white', 'and', 'brown', 'dog', 'is', 'running', 'through', 'a', 'snow', 'covered', 'field', 'The', 'white', 'and', 'brown', 'dog', 'is', 'running', 'over', 'the', 'surface', 'of', 'the', 'snow', 'A', 'man', 'in', 'a', 'hat', 'is', 'displaying', 'pictures', 'next', 'to', 'a', 'skier', 'in', 'a', 'blue', 'hat', 'A', 'man', 'skis', 'past', 'another', 'man', 'displaying', 'paintings', 'in', 'the', 'snow', 'A', 'person', 'wearing', 'skis', 'looking', 'at', 'framed', 'pictures', 'set', 'up', 'in', 'the', 'snow', 'A', 'skier', 'looks', 'at', 'framed', 'pictures', 'in', 'the', 'snow', 'next', 'to', 'trees', 'Man', 'on', 'skis', 'looking', 'at', 'artwork', 'for', 'sale', 'in', 'the', 'snow', 'A', 'collage', 'of', 'one', 'person', 'climbing', 'a', 'cliff', 'A', 'group', 'of', 'people', 'are', 'rock', 'climbing', 'on', 'a', 'rock', 'climbing', 'wall', 'A', 'group', 'of', 'people', 'climbing', 'a', 'rock', 'while', 'one', 'man', 'belays', 'Seven', 'climbers', 'are', 'ascending', 'a', 'rock', 'face', 'whilst', 'another', 'man', 'stands', 'holding', 'the', 'rope', 'Several', 'climbers', 'in', 'a', 'row', 'are', 'climbing', 'the', 'rock', 'while', 'the', 'man', 'in', 'red', 'watches', 'and', 'holds', 'the', 'line', 'A', 'brown', 'dog', 'chases', 'the', 'water', 'from', 'a', 'sprinkler', 'on', 'a', 'lawn', 'a', 'brown', 'dog', 'plays', 'with', 'the', 'hose', 'A', 'brown', 'dog', 'running', 'on', 'a', 'lawn', 'near', 'a', 'garden', 'hose', 'A', 'dog', 'is', 'playing', 'with', 'a', 'hose', 'Large', 'brown', 'dog', 'running', 'away', 'from', 'the', 'sprinkler', 'in', 'the', 'grass', 'A', 'dog', 'prepares', 'to', 'catch', 'a', 'thrown', 'object', 'in', 'a', 'field', 'with', 'nearby', 'cars', 'A', 'white', 'dog', 'is', 'about', 'to', 'catch', 'a', 'yellow', 'ball', 'in', 'its', 'mouth', 'A', 'white', 'dog', 'is', 'about', 'to', 'catch', 'a', 'yellow', 'dog', 'toy', 'A', 'white', 'dog', 'is', 'ready', 'to', 'catch', 'a', 'yellow', 'ball', 'flying', 'through', 'the', 'air', 'A', 'white', 'dog', 'running', 'after', 'a', 'yellow', 'ball', 'a', 'black', 'and', 'white', 'dog', 'jumping', 'in', 'the', 'air', 'to', 'get', 'a', 'toy', 'A', 'black', 'and', 'white', 'dog', 'jumps', 'up', 'towards', 'a', 'yellow', 'toy', 'A', 'dog', 'leaps', 'to', 'catch', 'a', 'ball', 'in', 'a', 'field', 'A', 'white', 'dog', 'is', 'trying', 'to', 'catch', 'a', 'ball', 'in', 'midair', 'over', 'a', 'grassy', 'field', 'The', 'white', 'dog', 'is', 'playing', 'in', 'a', 'green', 'field', 'with', 'a', 'yellow', 'toy', 'A', 'child', 'and', 'a', 'woman', 'are', 'at', 'waters', 'edge', 'in', 'a', 'big', 'city', 'a', 'large', 'lake', 'with', 'a', 'lone', 'duck', 'swimming', 'in', 'it', 'with', 'several', 'people', 'around', 'the', 'edge', 'of', 'it', 'A', 'little', 'boy', 'at', 'a', 'lake', 'watching', 'a', 'duck', 'A', 'young', 'boy', 'waves', 'his', 'hand', 'at', 'the', 'duck', 'in', 'the', 'water', 'surrounded', 'by', 'a', 'green', 'park', '"Two', 'people', 'are', 'at', 'the', 'edge', 'of', 'a', 'lake', '"A', 'couple', ...]
#converting to lowercase
words= [word.lower() for line in imageCaptions for word in line.split()] #We can see that we had both
wordCounts=collections.Counter(words)
wordCounts
Counter({'a': 60196,
'child': 1507,
'in': 18174,
'pink': 702,
'dress': 332,
'is': 9069,
'climbing': 490,
'up': 1215,
'set': 105,
'of': 6495,
'stairs': 109,
'an': 2325,
'entry': 1,
'way': 48,
'girl': 3277,
'going': 145,
'into': 1046,
'wooden': 278,
'building': 485,
'little': 1736,
'playhouse': 6,
'the': 17507,
'to': 3005,
'her': 1102,
'cabin': 4,
'black': 3620,
'dog': 7948,
'and': 8057,
'spotted': 36,
'are': 3365,
'fighting': 130,
'tri-colored': 12,
'playing': 1954,
'with': 7304,
'each': 399,
'other': 667,
'on': 10357,
'road': 380,
'white': 3706,
'brown': 2457,
'spots': 28,
'staring': 57,
'at': 2810,
'street': 908,
'two': 5132,
'dogs': 2095,
'different': 43,
'breeds': 5,
'looking': 682,
'pavement': 46,
'moving': 40,
'toward': 142,
'covered': 286,
'paint': 57,
'sits': 561,
'front': 1322,
'painted': 62,
'rainbow': 17,
'hands': 231,
'bowl': 29,
'sitting': 1322,
'large': 1207,
'small': 1257,
'grass': 1565,
'plays': 515,
'fingerpaints': 3,
'canvas': 6,
'it': 370,
'there': 253,
'pigtails': 14,
'painting': 43,
'young': 2587,
'outside': 756,
'man': 7137,
'lays': 55,
'bench': 360,
'while': 1807,
'his': 2255,
'by': 1167,
'him': 353,
'which': 27,
'also': 15,
'tied': 13,
'sleeping': 58,
'next': 714,
'shirtless': 99,
'lies': 43,
'park': 487,
'laying': 181,
'holding': 1211,
'leash': 125,
'ground': 332,
'orange': 694,
'hat': 633,
'starring': 8,
'something': 329,
'wears': 111,
'glasses': 184,
'gauges': 2,
'wearing': 2916,
'blitz': 1,
'beer': 43,
'can': 37,
'crocheted': 1,
'pierced': 6,
'ears': 65,
'rope': 237,
'net': 57,
'red': 2553,
'roping': 2,
'climbs': 197,
'bridge': 135,
'grips': 2,
'onto': 204,
'ropes': 37,
'playground': 198,
'running': 1996,
'grassy': 446,
'garden': 51,
'surrounded': 155,
'fence': 319,
'through': 1978,
'boston': 9,
'terrier': 30,
'lush': 8,
'green': 1156,
'runs': 906,
'near': 959,
'"a': 1234,
'shakes': 36,
'its': 863,
'head': 358,
'shore': 157,
'edge': 165,
'beach': 1018,
'ball': 1678,
'"dog': 8,
'feet': 79,
'water': 2676,
'standing': 1702,
'turned': 17,
'one': 751,
'side': 286,
'boy': 3514,
'smiles': 183,
'stony': 3,
'wall': 533,
'city': 309,
'overalls': 22,
'working': 23,
'stone': 123,
'aross': 1,
'walking': 1126,
'paved': 41,
'metal': 110,
'pole': 151,
'behind': 552,
'smiling': 434,
'shirt': 1735,
'blue': 2125,
'jeans': 207,
'rock': 730,
'leaps': 200,
'over': 1378,
'log': 53,
'grey': 232,
'leaping': 129,
'fallen': 67,
'tree': 390,
'mottled': 2,
'collar': 193,
'jumping': 1430,
'jumped': 33,
'stump': 27,
'snow': 1439,
'field': 1221,
'surface': 60,
'displaying': 5,
'pictures': 63,
'skier': 179,
'skis': 81,
'past': 145,
'another': 893,
'paintings': 4,
'person': 1515,
'framed': 7,
'looks': 490,
'trees': 247,
'artwork': 4,
'for': 901,
'sale': 7,
'collage': 2,
'cliff': 194,
'group': 1200,
'people': 2811,
'belays': 1,
'seven': 29,
'climbers': 27,
'ascending': 8,
'face': 445,
'whilst': 147,
'stands': 841,
'several': 297,
'row': 34,
'watches': 259,
'holds': 453,
'line': 145,
'chases': 97,
'from': 866,
'sprinkler': 52,
'lawn': 110,
'hose': 44,
'away': 153,
'prepares': 48,
'catch': 354,
'thrown': 44,
'object': 146,
'nearby': 140,
'cars': 61,
'about': 142,
'yellow': 1148,
'mouth': 913,
'toy': 549,
'ready': 119,
'flying': 165,
'air': 1008,
'after': 176,
'get': 100,
'jumps': 955,
'towards': 234,
'trying': 157,
'midair': 205,
'woman': 3304,
'waters': 29,
'big': 267,
'lake': 318,
'lone': 81,
'duck': 35,
'swimming': 433,
'around': 622,
'watching': 237,
'waves': 134,
'hand': 318,
'"two': 414,
'couple': 243,
'infant': 15,
'sit': 349,
'baby': 409,
'stroller': 26,
'their': 662,
'newborn': 8,
'under': 237,
'facing': 46,
'care': 3,
'along': 508,
'body': 185,
'outdoors': 155,
'surf': 72,
'lab': 24,
'tags': 7,
'frolicks': 2,
'splashes': 69,
'this': 118,
'splashing': 125,
'drilling': 5,
'hole': 35,
'ice': 218,
'frozen': 25,
'pond': 92,
'men': 1102,
'fishing': 102,
'play': 713,
'making': 142,
'turn': 69,
'soft': 13,
'sand': 446,
'together': 403,
'tan': 380,
'sandy': 105,
'uses': 26,
'picks': 12,
'crampons': 1,
'scale': 1,
'climber': 124,
'jacket': 649,
'pants': 234,
'scaling': 20,
'waterfall': 85,
'carries': 126,
'as': 841,
'he': 203,
'walks': 531,
'carrying': 396,
'has': 519,
'item': 18,
'wet': 169,
'kayak': 87,
'life': 82,
'jackets': 67,
'rowing': 33,
'canoe': 71,
'gentle': 1,
'ride': 248,
'courtyard': 28,
'catching': 106,
'snaps': 1,
'lunges': 6,
'chocolate': 13,
'too': 6,
'late': 3,
'captures': 1,
'driveway': 13,
'stick': 437,
'kneeling': 33,
'goalie': 42,
'hockey': 194,
'guarding': 9,
'goal': 81,
'kid': 182,
'rink': 30,
'male': 110,
'right': 85,
'"hockey': 1,
'crouches': 28,
'modern': 5,
'art': 38,
'structure': 73,
'glass': 59,
'reads': 46,
'newspaper': 34,
'sculpture': 22,
'office': 11,
'statue': 60,
'backpack': 145,
'buildings': 66,
'reading': 84,
'tent': 79,
'being': 326,
'enter': 8,
'setting': 78,
'hut': 12,
'iced': 3,
'snowy': 402,
'three': 1251,
'hill': 429,
'sky': 129,
'them': 216,
'stand': 498,
'kneels': 26,
'skyscraper': 6,
'very': 159,
'tall': 139,
'distance': 99,
'camera': 657,
'bites': 31,
'hard': 16,
'treat': 16,
'biting': 79,
'baked': 2,
'good': 7,
'putting': 32,
'both': 66,
'eats': 52,
'food': 83,
'table': 246,
'eating': 141,
'pizza': 10,
'tin': 1,
'dish': 10,
'mountainside': 25,
'check': 6,
'out': 705,
'view': 84,
'hilltop': 2,
'overlooking': 99,
'valley': 21,
'hang': 32,
'top': 463,
'overlook': 6,
'rest': 24,
'ledge': 73,
'above': 212,
'moutains': 1,
'down': 1755,
'many': 172,
'inflatable': 71,
'boats': 38,
'kayakers': 8,
'railing': 112,
'rafts': 6,
'below': 49,
'crowd': 494,
'jersey': 104,
'pose': 208,
'some': 579,
'multiracial': 1,
'posing': 281,
'picture': 407,
'"asian': 4,
'blond': 181,
'guy': 207,
'striped': 203,
'takeout': 1,
'television': 9,
'floor': 146,
'fast': 52,
'meal': 13,
'someone': 155,
'tv': 9,
'teens': 7,
'rail': 101,
'crowded': 74,
'takes': 166,
'jump': 394,
'skateboard': 426,
'performing': 195,
'trick': 375,
'leans': 57,
'skateboarder': 313,
'doing': 417,
'board': 129,
'platform': 48,
'skateboarders': 11,
'paddling': 47,
'river': 229,
'kayaking': 29,
'paddles': 33,
'boat': 265,
'paddle': 40,
'shallow': 128,
'girls': 832,
'ocean': 447,
'four': 459,
'children': 1135,
'pajamas': 18,
'have': 73,
'pillow': 15,
'fight': 76,
'kids': 337,
'pillow-fight': 1,
'bed': 153,
'having': 46,
'constructions': 1,
'workers': 18,
'beam': 20,
'taking': 195,
'break': 31,
'construction': 30,
'i-beam': 1,
'take': 77,
'seat': 49,
'steel': 4,
'boys': 651,
'puddle': 93,
'balloon': 41,
'mud': 111,
'sunny': 61,
'day': 135,
'appears': 31,
'wait': 43,
'hailing': 1,
'taxi': 4,
'signaling': 1,
'traffic': 46,
'blonde': 191,
'hair': 382,
'tube': 56,
'waving': 47,
'arm': 138,
'oncoming': 1,
'brochure': 2,
'train': 103,
'rides': 451,
'magizine': 1,
'book': 70,
'pamphlet': 1,
'rocky': 232,
'run': 346,
'across': 465,
'stones': 9,
'area': 362,
'background': 442,
'descends': 11,
'off': 719,
'end': 50,
'high': 273,
'diving': 73,
'pool': 679,
'dive': 11,
'window': 128,
'overshirt': 1,
'tank': 73,
'chrome': 1,
'door': 56,
'puts': 34,
'elevator': 6,
'light-colored': 4,
'light': 152,
'swim': 92,
'shorts': 352,
'trunks': 95,
'arms': 223,
'outstretched': 35,
'hiker': 80,
'bluff': 5,
'mountains': 194,
'ski': 94,
"'s": 379,
'landscape': 39,
'snow-covered': 47,
'mountain': 536,
'beautiful': 53,
'pauses': 10,
'mountaintop': 25,
'attempting': 56,
'purple': 301,
'low': 60,
'cut': 26,
'yard': 194,
'frisbee': 323,
'parking': 66,
'lot': 103,
'middle': 155,
'during': 166,
'heavy': 25,
'mat': 22,
'between': 119,
'suv': 13,
'pickup': 4,
'open': 166,
'busy': 98,
'terrain': 29,
'woolly': 1,
'doberman': 17,
'chasing': 181,
'catches': 123,
'tennis': 409,
'multicolor': 11,
'balloons': 25,
'night': 154,
'hot': 24,
'lit': 41,
'lined': 35,
'nighttime': 12,
'helmet': 334,
'bike': 841,
'miniature': 5,
'dirt': 577,
'bicycle': 409,
'race': 375,
'pedals': 3,
'quickly': 29,
'bmx': 76,
'eight': 15,
'gathered': 65,
'dark': 233,
'porch': 24,
'darkened': 10,
'room': 125,
'throwing': 96,
'cleans': 2,
'bubbles': 89,
'suds': 4,
'wiped': 2,
'clean': 7,
'foam': 19,
'ramp': 311,
'soapy': 1,
'getting': 119,
'cleaned': 1,
'slides': 88,
'slide': 293,
'wading': 53,
'toys': 48,
'floating': 37,
'backyard': 62,
'sliding': 117,
'colorful': 210,
'tubes': 2,
'falling': 74,
'colored': 159,
'wetsuit': 80,
'toddler': 165,
'waiting': 111,
'come': 11,
'so': 14,
'"while': 11,
'droplets': 6,
'fly': 35,
'sticks': 59,
'tongue': 98,
'.': 13,
'make': 55,
'faces': 66,
'look': 250,
'silly': 15,
'horse': 229,
'sweatshirt': 75,
'fire': 108,
'barrel': 22,
'lead': 16,
'horses': 90,
'bulldog': 9,
'pushing': 58,
'anouther': 1,
'skinny': 18,
'smaller': 62,
'int': 6,
'various': 19,
'sizes': 4,
'lady': 219,
'no': 78,
'dock': 93,
'deck': 37,
'closeup': 66,
'that': 386,
'paws': 30,
'lying': 95,
'resting': 42,
'tiled': 9,
'eyes': 87,
'rests': 23,
'patio': 20,
'bricks': 4,
'artificial': 9,
'safety': 31,
'harness': 80,
'indoor': 43,
'"the': 169,
'ring': 64,
'jumphouse': 1,
'teenage': 72,
'seating': 6,
'inflated': 5,
'family': 84,
'tractor': 9,
'polaris': 3,
'vehicle': 76,
'played': 18,
'wheeler': 12,
'riding': 884,
'atv': 39,
'costume': 128,
'left': 80,
'sequined': 4,
'feather': 10,
'sidewalk': 357,
'salmon': 2,
'bikini': 64,
';': 31,
'outfit': 156,
'drinking': 92,
'pop': 7,
'approached': 2,
'flamboyant': 3,
'dressed': 541,
'feathered': 7,
'headress': 1,
'skiiers': 9,
'forest': 156,
'skiing': 109,
'wooded': 92,
'cross-country': 20,
'skiers': 36,
'woodland': 21,
'trail': 142,
'woods': 209,
'hikers': 35,
'pathway': 11,
'path': 313,
'happily': 26,
'energetic': 1,
'mother': 39,
'boardwalk': 23,
'sea': 26,
'pier': 33,
'evening': 8,
'pony': 12,
'wintertime': 2,
'atop': 35,
'draft': 3,
'n': 2,
'daft': 1,
'pull': 32,
'cart': 79,
'golden': 103,
'sleigh': 3,
'driven': 10,
'coat': 252,
'pulling': 108,
'carriage': 19,
'sled': 114,
'steered': 2,
'sheer': 16,
'using': 58,
'rappels': 2,
'steep': 61,
'incline': 11,
'vest': 91,
'inside': 168,
'dome': 3,
'shaft': 1,
'cave': 20,
'shows': 30,
'spelunkers': 2,
'cavern': 1,
'bathed': 1,
'sunlight': 11,
'rocks': 249,
'backpackers': 2,
'lay': 20,
'dry': 78,
'camp': 6,
'gear': 113,
'chalk': 15,
'portrait': 6,
'stream': 111,
'drawing': 13,
'barn-like': 1,
'elaborate': 11,
'illustration': 2,
'deep': 80,
'pile': 75,
'fountain': 178,
'fountains': 14,
'sprayed': 24,
'corgi': 4,
'tunnel': 66,
'course': 131,
'obstacle': 122,
'obedience': 3,
'swimsuit': 61,
'handrail': 31,
'bathing': 117,
'suit': 237,
'spray': 34,
'soaked': 12,
'jet': 21,
'sticking': 49,
'shower': 20,
'gets': 106,
'underwater': 52,
'red-headed': 4,
'swims': 103,
'gun': 39,
'foot': 38,
'aims': 5,
'fireplace': 10,
'new': 17,
'light-up': 1,
'pointed': 12,
'alone': 34,
'jagged': 7,
'snowmobile': 13,
'mid': 8,
'snowmobiler': 2,
'flies': 100,
'rider': 182,
'or': 82,
'machine': 30,
'pine': 22,
'rural': 41,
'snowmobiles': 4,
'helmets': 35,
'goggles': 65,
'snowmobiling': 2,
'helmeted': 15,
'drive': 16,
'atvs': 3,
'heads': 47,
'wheel': 57,
'wheelers': 3,
'empty': 47,
'all': 121,
'!': 9,
'gin': 1,
'four-wheeler': 5,
'airborne': 70,
'quad': 6,
'harvested': 2,
'cornfield': 1,
's': 2,
'happy': 19,
'od': 1,
'playfully': 31,
'soccer': 564,
'tucked': 1,
't-shirt': 146,
'artist': 13,
'paints': 12,
'clouds': 25,
'braids': 6,
'colors': 21,
'paper': 72,
'cyclist': 72,
'curved': 4,
'aerodynamic': 2,
'sharp': 20,
'curve': 24,
'pedaling': 3,
'"man': 27,
'biker': 171,
'fetch': 31,
'pounces': 7,
'cine': 1,
'old': 170,
'fashioned': 6,
'video': 39,
'steadies': 2,
'aim': 4,
'rosy': 1,
'cheeks': 6,
'lips': 17,
'border': 9,
'collie': 30,
'audience': 36,
'dug': 3,
'agile': 1,
'onlookers': 47,
'closely': 5,
'smooth': 6,
'stacking': 1,
'against': 193,
'backdrop': 9,
'shoes': 81,
'rappeling': 1,
'headlamp': 2,
'attached': 51,
'snakeskin': 1,
'sprays': 11,
'frog': 5,
'public': 47,
'wood': 69,
'barrior': 1,
'animal': 92,
'bird': 186,
'seeds': 6,
'sunflower': 1,
'clinging': 5,
'finger': 31,
'wades': 12,
'guided': 3,
'laughs': 32,
'playful': 7,
'beige': 51,
'torwards': 1,
'outdoor': 127,
'handstand': 48,
'does': 226,
'sheets': 5,
'upside': 89,
'snowboard': 87,
'surfboard': 146,
'bikes': 83,
'traveling': 16,
'worn': 5,
'motorcycles': 27,
'motorbikes': 7,
'follow': 14,
'dirt-bikes': 1,
'off-road': 9,
'garment': 1,
'flag': 91,
'crescent': 1,
'moon': 3,
'star': 11,
'gown': 9,
'muslim': 3,
'helps': 29,
'wave': 314,
'half': 29,
'naked': 47,
'chair': 125,
'older': 206,
'back': 354,
'relaxes': 12,
'adobe': 1,
'where': 37,
'bicycles': 52,
'propped': 4,
'cap': 170,
'relaxing': 15,
'folding': 10,
'"topless': 2,
'slippers': 2,
'hauling': 1,
'retrieve': 9,
'mouths': 37,
'tug': 29,
'chew': 16,
'"there': 43,
'party': 55,
'streets': 28,
'drink': 101,
'they': 117,
'women': 639,
'parade': 63,
'neck-deep': 1,
'"brown': 14,
'creating': 8,
'splash': 42,
'seaweed': 10,
'lav': 1,
'sandals': 17,
'pinstripe': 2,
'snows': 4,
'furry': 52,
'attempts': 52,
'itself': 19,
'shaking': 67,
'self': 5,
'backpacks': 29,
'placed': 9,
'cardboard': 24,
'bus': 74,
'station': 32,
'bouncing': 28,
'folded': 8,
'beds': 3,
'bedroom': 5,
'snowboarder': 236,
'slope': 72,
'boarders': 1,
'snowboarders': 12,
'clothing': 122,
'store': 105,
'opening': 13,
'stores': 8,
'piece': 63,
'attire': 25,
'car': 419,
'strip': 22,
'boots': 55,
'stepping': 8,
'van': 16,
'wear': 47,
'game': 359,
'plants': 38,
'crossing': 57,
'greenery': 6,
'suspension': 1,
'tropical': 12,
'caution': 6,
'sign': 234,
'beside': 243,
'bright': 120,
'truck': 87,
'others': 147,
'watch': 220,
'helping': 27,
'step': 29,
'pulled': 39,
'passengers': 7,
'load': 2,
'brightly': 30,
'poses': 140,
'pig': 5,
'hugs': 23,
'who': 206,
'"blond': 4,
'embracing': 8,
'hugging': 57,
'pink-hooded': 1,
'stretch': 11,
'bicyclist': 63,
'cross': 44,
'spandex': 2,
'biking': 28,
'jogging': 25,
'headset': 5,
'walkman': 1,
'jogs': 13,
'headphones': 38,
'plant': 16,
'corner': 57,
'bicyclists': 24,
'intersection': 14,
'bikers': 29,
'stop': 34,
'town': 24,
'without': 25,
'guiding': 5,
'wagon': 25,
'escorts': 1,
'leading': 20,
'horse-drawn': 1,
'shetland': 1,
'hits': 48,
'tee': 6,
'practices': 10,
'hitting': 35,
'baseball': 285,
'bats': 3,
'put': 24,
'batting': 7,
'cage': 14,
'spins': 15,
'sun': 98,
'she': 88,
'trumpet': 8,
...})
#words sorted by word count
for word, count in wordCounts.most_common(20):
print(word, ": ", count)
a : 60196 in : 18174 the : 17507 on : 10357 is : 9069 and : 8057 dog : 7948 with : 7304 man : 7137 of : 6495 two : 5132 white : 3706 black : 3620 boy : 3514 are : 3365 woman : 3304 girl : 3277 to : 3005 wearing : 2916 people : 2811
#bar graph
wordCountsList = wordCounts.most_common(20) #container function
mostCommonWordsDf = pd.DataFrame(wordCountsList, columns = ['Word', 'Count'])
mostCommonWordsDf.plot.bar(x='Word',y='Count',width=0.5,color='red')
plt.title("Top 20 most occuring words")
plt.xlabel("Words")
plt.ylabel("Counts")
Text(0, 0.5, 'Counts')
#wordcloud
wordcloud = WordCloud(width = 1000, height = 500).generate_from_frequencies(wordCounts)
plt.figure(figsize=(8,8))
plt.imshow(wordcloud)
<matplotlib.image.AxesImage at 0x2e04e48f710>
#Looking at non-stopword words using frequency
sansStopWordsDict = {}
tempDict = {}
i = 0
for word, count in wordCounts.most_common(36):
if word not in STOPWORDS:
print(word, ": ", count)
sansStopWordsDict[word] = count
if i < 10:
tempDict[word] = count
i += 1
plt.title("Top 10 most occuring words")
plt.bar(*zip(*tempDict.items()))
plt.show()
plt.bar(range(len(sansStopWordsDict)), sansStopWordsDict.values(), align='center')
plt.xticks(range(len(sansStopWordsDict)), list(sansStopWordsDict.keys()))
plt.title("Top " + str(i) + " most occuring words")
plt.xlabel("Words")
plt.ylabel("Counts")
plt.xticks(rotation=90)
dog : 7948 man : 7137 two : 5132 white : 3706 black : 3620 boy : 3514 woman : 3304 girl : 3277 wearing : 2916 people : 2811 water : 2676 young : 2587 red : 2553 brown : 2457 blue : 2125 dogs : 2095 running : 1996 playing : 1954 little : 1736 shirt : 1735
(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19]),
[Text(0, 0, 'dog'),
Text(1, 0, 'man'),
Text(2, 0, 'two'),
Text(3, 0, 'white'),
Text(4, 0, 'black'),
Text(5, 0, 'boy'),
Text(6, 0, 'woman'),
Text(7, 0, 'girl'),
Text(8, 0, 'wearing'),
Text(9, 0, 'people'),
Text(10, 0, 'water'),
Text(11, 0, 'young'),
Text(12, 0, 'red'),
Text(13, 0, 'brown'),
Text(14, 0, 'blue'),
Text(15, 0, 'dogs'),
Text(16, 0, 'running'),
Text(17, 0, 'playing'),
Text(18, 0, 'little'),
Text(19, 0, 'shirt')])
#wordcloud of non-stopwords
wordcloud = WordCloud(width = 1000, height = 500).generate_from_frequencies(sansStopWordsDict)
plt.figure(figsize=(8,8))
plt.imshow(wordcloud)
<matplotlib.image.AxesImage at 0x2e04eb3c9e8>
#mean caption length
captionDf=df.copy()
#captionDf
captionDf['caption_word_count'] = captionDf["Captions"].str.split().str.len()
#captionDf['caption_word_count']
mean = sum(captionDf['caption_word_count'])/len(captionDf['caption_word_count'])
print("Mean of caption length:", mean)
#count of no of captions with the specified char count (x axis)
#Show the counts of observations in each categorical bin using bars
sns.set(style="whitegrid", font_scale = 1)
plt.figure(figsize=(8, 5))
ax = sns.countplot(captionDf['caption_word_count'], color="r")
plt.xlabel('Mean of Word Count of captions ', fontsize=16)
plt.ylabel('Count', fontsize=16)
ax.axvline(mean, color='b', linestyle='--')
plt.legend({'Mean: {:.2f}'.format(mean): mean})
Mean of caption length: 10.36576442961315
C:\Users\subha\anaconda3\envs\hello-tf\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. FutureWarning
<matplotlib.legend.Legend at 0x2e0544342b0>
median = np.median(captionDf['caption_word_count'])
print("Median value of caption chars:", median)
sns.set(style="whitegrid", font_scale = 1)
plt.figure(figsize=(8, 5))
ax = sns.countplot(captionDf['caption_word_count'], color="r")
plt.xlabel('Median of Word Count of captions ', fontsize=16)
plt.ylabel('Count', fontsize=16)
ax.axvline(median, color='b', linestyle='--')
plt.legend({'Median: {:.2f}'.format(mean): mean})
Median value of caption chars: 10.0
C:\Users\subha\anaconda3\envs\hello-tf\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. FutureWarning
<matplotlib.legend.Legend at 0x2e049055cc0>
Conclusion: Most of the captions have 10 to 11 words
#statistics of images and captions together
#Images
fig = plt.figure(figsize=(15,15)) #for the set of 5 images
count = 1
for path in df[:25].Path.unique():
captions = list(df["Captions"].loc[df.Path == path].values)
displayImage = load_img(path, target_size=(299,299,3))
ax = fig.add_subplot(5, 2, count) #for individual image
plt.axis('off') # we don't want both x and y axes for caption
ax.plot()
ax.set_xlim(0,1)
ax.set_ylim(0,len(captions)) #to decide the spacing between each line of caption
for i, caption in enumerate(captions):
ax.text(0,i,caption,fontsize=15) #to display the captions
count+=1
plt.tight_layout() #to make sure that image and caption don't overlap
ax = fig.add_subplot(5,2,count)
ax.imshow(displayImage)
plt.axis('off')
count +=1
plt.show()
#data cleaning
#conv to lowercase
#remove punctuation
#remove numbers
#not removing stopwords but one letter words will be removed
#no stemming
# Data cleaning
# returns a mapping table that can be used with the translate() method to replace specified characters
remove_punctuation = str.maketrans('', '', string.punctuation)
for i in range(len(imageCaptions)):
line = imageCaptions[i]
line = line.split()
line = [word.lower() for word in line] # convert to lower case
line = [word.translate(remove_punctuation) for word in line]# remove punctuation from each word
line = [word for word in line if len(word) > 1] # remove hanging single characters
line = [word for word in line if word.isalpha()] # remove words with numbers in them
imageCaptions[i]= ' '.join(line) # store as string
#Create a list which contains all the captions
#add the <start> & <end> token to all those captions as well
imageCaptions=['<start>' + ' ' + line + ' ' + '<end>' for line in imageCaptions]
#Create a list which contains all the path to the images
all_img_path= imageLocations#write your code here
print("Total captions present in the dataset: "+ str(len(imageCaptions)))
print("Total images*5 present in the dataset: " + str(len(all_img_path)))
Total captions present in the dataset: 40455 Total images*5 present in the dataset: 40455
imageCaptions[0:5]
['<start> child in pink dress is climbing up set of stairs in an entry way <end>', '<start> girl going into wooden building <end>', '<start> little girl climbing into wooden playhouse <end>', '<start> little girl climbing the stairs to her playhouse <end>', '<start> little girl in pink dress going into wooden cabin <end>']
Pre-Processing text(captions):
top_voc = 5000
tokenizer =Tokenizer(num_words=top_voc+1,filters='!"#$%&()*+.,-/:;=?@[\]^_`{|}~ ', lower=True, char_level=False, oov_token="UNK")
# Create word-to-index and index-to-word mappings
#Updates internal vocabulary based on a list of texts
tokenizer.fit_on_texts(imageCaptions)
#Transforms each text in texts to a sequence of integers
train_seqs = tokenizer.texts_to_sequences(imageCaptions)
print(train_seqs)
tokenizer.word_index['PAD'] = 0
tokenizer.index_word[0] = 'PAD'
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10, 29, 9, 3], [2, 11, 262, 6, 157, 53, 345, 12, 9, 49, 6, 170, 3], [2, 11, 4, 28, 89, 98, 2057, 23, 171, 3], [2, 11, 373, 28, 89, 98, 8, 260, 3], [2, 11, 10, 3968, 8, 260, 7, 21, 1, 98, 3], [2, 11, 10, 260, 7, 21, 745, 813, 1, 98, 3], [2, 5, 11, 10, 2356, 554, 7, 21, 260, 8, 28, 89, 98, 3], [2, 43, 34, 6, 211, 615, 3], [2, 37, 19, 115, 6, 26, 3969, 3], [2, 37, 19, 4, 88, 246, 211, 328, 23, 5, 117, 3], [2, 50, 43, 3970, 240, 5, 26, 814, 23, 5, 245, 3], [2, 5, 50, 43, 246, 6, 26, 814, 6, 245, 3], [2, 15, 8, 14, 9, 7, 32, 4, 126, 662, 293, 56, 14, 174, 3], [2, 15, 8, 14, 9, 7, 32, 33, 5, 41, 3], [2, 1904, 937, 7, 32, 4, 5, 41, 3], [2, 1904, 937, 7, 32, 6, 2058, 57, 41, 4, 48, 12, 14, 174, 3], [2, 9, 70, 6, 5, 57, 41, 66, 193, 174, 3], [2, 9, 839, 75, 159, 66, 5, 287, 3], [2, 14, 9, 839, 6, 5, 281, 12, 64, 10, 28, 89, 40, 3], [2, 9, 10, 89, 40, 23, 485, 3], [2, 14, 9, 34, 10, 26, 40, 6, 5, 287, 66, 5, 24, 3], [2, 14, 9, 10, 27, 554, 39, 66, 24, 10, 159, 1335, 20, 80, 188, 3], [2, 16, 261, 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7, 49, 213, 183, 502, 6, 5, 64, 85, 20, 5, 125, 3], [2, 18, 4, 15, 587, 103, 213, 1, 502, 23, 5, 64, 3], [2, 18, 49, 6, 5, 64, 213, 88, 379, 502, 3], [2, 79, 4, 1377, 45, 63, 24, 74, 104, 3], [2, 55, 12, 292, 97, 8, 79, 4, 365, 601, 67, 63, 5, 24, 10, 93, 954, 20, 5, 94, 3], [2, 292, 79, 4, 1377, 3], [2, 79, 4, 1377, 3], [2, 5, 292, 79, 21, 1377, 145, 63, 5, 24, 3], [2, 43, 67, 63, 91, 76, 11, 116, 6, 3], [2, 19, 10, 1, 6, 7, 45, 63, 91, 3], [2, 37, 19, 21, 89, 325, 1295, 45, 63, 91, 3], [2, 37, 19, 10, 527, 3615, 7, 45, 63, 5, 91, 3], [2, 25, 19, 10, 24, 737, 244, 63, 504, 91, 3], [2, 15, 9, 10, 27, 899, 85, 20, 258, 12, 24, 3], [2, 15, 9, 10, 26, 253, 110, 28, 1288, 4, 29, 68, 8, 7, 115, 87, 12, 91, 3], [2, 27, 9, 115, 87, 12, 91, 10, 131, 4, 29, 68, 3], [2, 9, 10, 131, 4, 75, 68, 246, 87, 12, 5, 24, 3], [2, 5, 9, 7, 115, 87, 12, 5, 24, 10, 131, 3], [2, 11, 7, 1160, 6, 5, 170, 66, 13, 879, 2210, 3], [2, 11, 7, 1, 66, 28, 2888, 35, 72, 11, 7, 1, 1549, 74, 742, 2888, 3], [2, 11, 103, 4, 48, 12, 28, 2888, 943, 35, 72, 11, 1038, 5, 943, 3], [2, 11, 10, 1, 96, 6, 29, 866, 422, 6, 5, 160, 66, 13, 2888, 2210, 3], [2, 185, 7, 11, 608, 28, 2888, 943, 3], [2, 11, 3415, 240, 5, 82, 128, 3], [2, 11, 4, 26, 38, 657, 20, 469, 82, 3], [2, 11, 21, 26, 3], [2, 11, 4, 26, 38, 115, 82, 250, 3], [2, 82, 350, 1066, 211, 3], [2, 16, 67, 63, 5, 91, 3], [2, 28, 371, 199, 43, 2685, 63, 5, 91, 35, 72, 43, 116, 6, 3], [2, 18, 418, 37, 16, 63, 5, 91, 3], [2, 18, 4, 30, 7, 1534, 25, 43, 63, 91, 3], [2, 18, 565, 37, 16, 63, 91, 76, 72, 16, 199, 3], [2, 107, 787, 3], [2, 2456, 439, 7, 2552, 10, 30, 336, 3], [2, 142, 107, 1032, 3], [2, 220, 251, 3], [2, 44, 187, 2558, 3], [2, 55, 12, 118, 22, 2830, 85, 20, 511, 3], [2, 118, 22, 249, 4, 48, 12, 511, 3], [2, 118, 836, 39, 85, 20, 511, 8, 300, 8, 449, 3], [2, 5, 13, 2129, 17, 2681, 56, 5, 511, 3], [2, 13, 79, 8, 13, 495, 17, 300, 20, 143, 92, 10, 511, 4, 5, 129, 3], [2, 1503, 9, 7, 60, 6, 5, 1115, 23, 5, 64, 3], [2, 1503, 60, 6, 5, 64, 85, 20, 100, 330, 3], [2, 1503, 109, 112, 5, 1855, 287, 3], [2, 5, 1503, 7, 6, 64, 60, 112, 5, 1115, 3], [2, 5, 1503, 7, 60, 6, 5, 64, 23, 5, 1, 3], [2, 407, 4, 88, 517, 122, 430, 745, 3], [2, 18, 4, 217, 88, 517, 122, 3], [2, 18, 4, 88, 517, 122, 430, 87, 12, 342, 597, 3], [2, 18, 4, 88, 517, 122, 123, 342, 745, 1873, 3], [2, 18, 21, 88, 517, 122, 53, 1873, 12, 2731, 3], [2, 835, 4, 58, 38, 7, 1, 133, 3], [2, 835, 10, 80, 57, 1505, 8, 80, 14, 1505, 70, 1520, 3], [2, 1317, 835, 7, 1025, 56, 692, 4, 153, 3], [2, 11, 21, 57, 1585, 70, 36, 1960, 3], [2, 5, 835, 4, 26, 8, 58, 110, 711, 975, 154, 52, 5, 133, 3], [2, 16, 1320, 28, 231, 380, 128, 148, 187, 4, 30, 8, 58, 1, 3676, 3], [2, 43, 4, 58, 38, 7, 935, 928, 284, 465, 84, 12, 1828, 1296, 3], [2, 25, 16, 7, 935, 30, 8, 58, 1, 84, 25, 11, 128, 3], [2, 25, 16, 7, 935, 284, 1, 84, 25, 11, 128, 3], [2, 285, 1320, 5, 128, 12, 11, 10, 30, 8, 58, 614, 6, 29, 128, 3], [2, 237, 7, 73, 77, 52, 5, 188, 12, 133, 3], [2, 25, 11, 655, 215, 5, 94, 8, 297, 74, 644, 251, 6, 683, 329, 3], [2, 11, 6, 77, 4, 251, 3], [2, 11, 73, 139, 36, 1358, 179, 3], [2, 11, 73, 77, 6, 316, 3], [2, 208, 12, 61, 165, 56, 54, 352, 3714, 10, 251, 4, 5, 129, 3], [2, 13, 61, 1095, 66, 107, 1032, 3], [2, 13, 61, 165, 254, 352, 1408, 713, 44, 187, 2558, 3], [2, 13, 61, 165, 23, 1, 352, 4, 5, 251, 3], [2, 13, 61, 49, 85, 20, 324, 352, 3], [2, 185, 17, 13, 61, 73, 655, 81, 4, 5, 44, 187, 1620, 3], [2, 5, 13, 61, 8, 93, 466, 17, 6, 5, 188, 12, 142, 151, 3], [2, 13, 61, 6, 655, 6, 5, 188, 12, 1, 151, 4, 5, 1479, 3], [2, 13, 61, 877, 6, 705, 133, 10, 93, 655, 3], [2, 13, 61, 249, 921, 74, 73, 93, 466, 6, 142, 151, 3], [2, 272, 466, 4, 5, 251, 3], [2, 380, 569, 6, 107, 151, 3], [2, 11, 998, 4, 48, 12, 251, 3], [2, 11, 7, 998, 36, 2247, 710, 962, 3], [2, 11, 6, 139, 1, 194, 4, 5, 251, 3], [2, 11, 6, 77, 1, 20, 222, 3], [2, 11, 124, 139, 6, 220, 179, 209, 175, 4, 5, 251, 3], [2, 11, 124, 139, 6, 220, 316, 112, 54, 222, 3], [2, 11, 78, 3208, 77, 6, 220, 316, 56, 175, 3], [2, 5, 11, 6, 5, 77, 7, 49, 6, 697, 133, 3], [2, 27, 9, 8, 15, 9, 166, 63, 143, 92, 4, 5, 41, 3], [2, 27, 9, 7, 1708, 5, 253, 12, 15, 9, 3], [2, 217, 15, 9, 7, 34, 10, 299, 27, 9, 4, 577, 3], [2, 13, 31, 17, 609, 4, 51, 3], [2, 13, 31, 17, 609, 4, 5, 117, 3], [2, 480, 2814, 211, 8, 538, 328, 46, 54, 492, 2959, 3], [2, 42, 10, 795, 351, 7, 60, 121, 1, 193, 1, 328, 3], [2, 18, 618, 193, 1688, 824, 328, 3], [2, 18, 60, 121, 28, 274, 211, 8, 1, 328, 360, 251, 3], [2, 5, 18, 7, 60, 6, 1, 1358, 328, 3], [2, 54, 486, 10, 57, 1300, 23, 3652, 3], [2, 486, 8, 2108, 4, 1, 291, 3], [2, 140, 12, 2108, 10, 196, 57, 486, 3], [2, 4955, 12, 1728, 3966, 36, 6, 57, 486, 4, 5, 234, 3], [2, 5, 414, 7, 3440, 33, 5, 205, 240, 57, 486, 4, 5, 234, 3], [2, 43, 7, 34, 4, 5, 494, 24, 32, 112, 126, 155, 3], [2, 37, 16, 7, 49, 6, 273, 179, 187, 4, 374, 3], [2, 50, 16, 103, 4, 374, 425, 8, 389, 187, 10, 374, 3], [2, 283, 735, 25, 259, 16, 34, 4, 5, 374, 3], [2, 5, 37, 263, 103, 36, 4, 5, 494, 24, 3], [2, 19, 4, 1281, 8, 16, 4, 340, 4, 3735, 3], [2, 37, 19, 4, 88, 1281, 389, 359, 71, 1990, 326, 3], [2, 25, 19, 21, 88, 1281, 326, 10, 72, 25, 16, 4, 5, 129, 3], [2, 25, 19, 21, 1281, 665, 4, 54, 347, 3], [2, 13, 59, 17, 4, 340, 4, 545, 1790, 3], [2, 263, 7, 607, 452, 488, 104, 28, 371, 276, 1, 716, 3], [2, 37, 16, 7, 295, 6, 20, 452, 488, 1269, 104, 11, 3], [2, 50, 16, 21, 14, 38, 684, 219, 452, 488, 3], [2, 25, 16, 657, 20, 1529, 5, 1, 452, 488, 71, 11, 3], [2, 5, 25, 16, 7, 607, 5, 488, 276, 5, 391, 3], [2, 871, 527, 40, 3], [2, 5, 61, 17, 6, 133, 10, 871, 14, 40, 3], [2, 13, 61, 17, 288, 20, 1529, 318, 283, 54, 527, 1161, 40, 3], [2, 13, 61, 1897, 427, 40, 3], [2, 13, 1290, 4, 89, 8, 15, 390, 52, 834, 3], [2, 137, 7, 288, 20, 1043, 54, 26, 342, 105, 289, 3], [2, 255, 1441, 749, 6, 26, 105, 3], [2, 255, 277, 2723, 29, 68, 6, 5, 281, 12, 26, 342, 105, 3], [2, 43, 111, 10, 105, 4, 5, 41, 3], [2, 43, 10, 29, 68, 6, 26, 342, 105, 3], [2, 11, 4, 26, 429, 420, 7, 45, 240, 2815, 3], [2, 11, 4, 26, 420, 406, 33, 5, 65, 10, 1356, 337, 3], [2, 11, 10, 1457, 7, 516, 46, 591, 148, 7, 1538, 24, 3], [2, 404, 11, 1, 3], [2, 11, 7, 516, 240, 29, 1457, 3], [2, 498, 12, 11, 53, 137, 21, 30, 38, 3], [2, 11, 123, 2338, 137, 10, 88, 2373, 8, 30, 38, 3], [2, 11, 4, 15, 123, 137, 3], [2, 1, 1, 29, 137, 19, 1, 474, 20, 5, 1487, 10, 163, 3], [2, 5, 11, 7, 53, 137, 66, 759, 158, 3], [2, 9, 67, 63, 5, 428, 3], [2, 9, 335, 63, 5, 24, 66, 3918, 3], [2, 299, 284, 9, 45, 84, 5, 162, 12, 197, 63, 5, 24, 3], [2, 150, 9, 45, 84, 12, 197, 63, 5, 24, 3], [2, 5, 58, 9, 7, 45, 84, 12, 1330, 63, 5, 24, 3], [2, 16, 45, 84, 12, 426, 35, 29, 985, 199, 3], [2, 13, 95, 17, 115, 193, 689, 8, 45, 63, 222, 3], [2, 13, 95, 45, 84, 5, 893, 63, 5, 24, 3], [2, 13, 95, 878, 20, 145, 84, 893, 2450, 6, 54, 258, 12, 24, 3], [2, 13, 59, 145, 84, 5, 426, 63, 5, 175, 3], [2, 27, 9, 7, 32, 46, 44, 66, 2733, 205, 3], [2, 27, 9, 70, 121, 142, 51, 3], [2, 9, 7, 32, 33, 5, 44, 3], [2, 9, 70, 33, 5, 44, 3], [2, 1053, 1116, 27, 9, 7, 32, 33, 28, 279, 14, 431, 155, 3], [2, 859, 733, 328, 46, 653, 776, 377, 8, 220, 1983, 3], [2, 22, 618, 195, 328, 46, 1965, 10, 222, 3], [2, 22, 184, 121, 211, 328, 46, 220, 377, 3], [2, 100, 859, 17, 618, 538, 8, 824, 328, 46, 222, 3], [2, 47, 22, 17, 90, 121, 211, 8, 538, 328, 46, 222, 3], [2, 480, 7, 60, 112, 179, 23, 5, 803, 12, 100, 1493, 251, 3], [2, 11, 109, 66, 251, 3], [2, 11, 10, 421, 676, 78, 4, 48, 12, 107, 3], [2, 5, 42, 7, 60, 4, 5, 251, 3], [2, 5, 266, 7, 90, 23, 5, 1493, 251, 3], [2, 55, 12, 22, 660, 6, 1106, 3], [2, 55, 12, 22, 39, 6, 1106, 4, 283, 2576, 490, 3], [2, 55, 12, 22, 39, 6, 50, 1106, 12, 54, 119, 3], [2, 55, 12, 22, 113, 6, 1106, 3], [2, 118, 22, 204, 4, 28, 341, 2363, 3], [2, 13, 61, 4, 3804, 169, 39, 4, 1475, 787, 3], [2, 13, 61, 4, 1, 113, 85, 20, 135, 4, 5, 671, 8, 177, 23, 958, 501, 3], [2, 13, 61, 4, 1562, 177, 23, 28, 1453, 897, 1396, 33, 5, 127, 3], [2, 13, 61, 4, 1569, 3158, 169, 39, 66, 135, 177, 23, 28, 1578, 4, 5, 671, 3], [2, 13, 22, 10, 159, 2726, 113, 4, 387, 51, 3], [2, 19, 356, 4, 15, 8, 14, 3], [2, 19, 106, 4, 15, 8, 14, 10, 3701, 931, 78, 4, 5, 294, 12, 69, 10, 14, 502, 8, 15, 122, 98, 4, 62, 176, 3], [2, 1623, 326, 4, 5, 996, 21, 28, 4513, 290, 3], [2, 18, 10, 1460, 159, 106, 4, 15, 8, 14, 4, 563, 3], [2, 5, 227, 4, 5, 69, 7, 21, 14, 8, 15, 340, 8, 7, 144, 14, 502, 3], [2, 37, 263, 536, 99, 4, 5, 1200, 91, 35, 227, 199, 3], [2, 18, 4536, 24, 6, 50, 43, 238, 7, 4, 91, 3], [2, 18, 7, 418, 626, 12, 24, 46, 137, 4, 5, 703, 91, 3], [2, 18, 7, 418, 24, 6, 43, 4, 342, 132, 91, 4, 770, 155, 3], [2, 43, 442, 63, 645, 91, 570, 12, 24, 35, 28, 371, 1675, 626, 12, 24, 6, 230, 3], [2, 1, 588, 1363, 75, 941, 215, 9, 10, 28, 279, 68, 3], [2, 588, 1, 23, 1336, 9, 3], [2, 9, 8, 588, 17, 334, 6, 348, 3], [2, 1653, 588, 334, 10, 15, 8, 27, 9, 4, 26, 253, 3], [2, 5, 58, 8, 15, 588, 8, 5, 27, 8, 15, 9, 17, 334, 3], [2, 43, 7, 32, 297, 74, 342, 91, 8, 28, 371, 6, 770, 490, 3], [2, 43, 7, 32, 297, 74, 18, 39, 56, 703, 91, 3], [2, 43, 34, 10, 24, 731, 35, 18, 3288, 10, 2545, 3], [2, 18, 8, 62, 1833, 17, 34, 81, 56, 1200, 91, 3], [2, 18, 199, 43, 86, 66, 50, 91, 3], [2, 259, 16, 1085, 1284, 3], [2, 16, 2150, 606, 1284, 3], [2, 16, 2150, 606, 1284, 35, 72, 1284, 1324, 104, 163, 3], [2, 37, 255, 16, 7, 1085, 752, 1284, 3], [2, 37, 16, 7, 1085, 1284, 148, 7, 262, 36, 3], [2, 11, 107, 115, 52, 28, 1321, 107, 3], [2, 28, 350, 7, 2059, 28, 229, 187, 82, 128, 3], [2, 42, 4, 89, 246, 1394, 250, 128, 187, 4, 44, 8, 229, 3], [2, 42, 4, 58, 96, 7, 115, 52, 44, 187, 203, 3], [2, 185, 7, 350, 1204, 142, 1059, 3], [2, 18, 8, 37, 19, 165, 6, 671, 82, 3], [2, 25, 19, 8, 231, 42, 17, 49, 66, 5, 122, 12, 82, 844, 3], [2, 13, 79, 165, 6, 54, 82, 3], [2, 13, 22, 17, 49, 6, 427, 82, 844, 3], [2, 13, 22, 165, 4, 5, 409, 6, 28, 2313, 82, 844, 3], [2, 488, 1, 13, 61, 172, 791, 56, 436, 4, 5, 696, 3], [2, 223, 1802, 1253, 32, 33, 1, 3], [2, 15, 8, 14, 436, 1236, 488, 10, 22, 33, 5, 696, 3], [2, 22, 1238, 223, 3805, 279, 1253, 4, 5, 696, 3], [2, 5, 436, 902, 5, 1253, 3], [2, 27, 9, 4, 88, 38, 70, 33, 51, 3], [2, 2799, 9, 10, 88, 38, 32, 33, 51, 3], [2, 1455, 9, 21, 88, 433, 8, 32, 3], [2, 27, 9, 4, 88, 366, 70, 6, 58, 41, 3], [2, 9, 10, 88, 96, 32, 4, 51, 3], [2, 255, 43, 397, 3], [2, 16, 7, 132, 4, 279, 24, 3], [2, 43, 3624, 74, 5, 24, 3], [2, 263, 1120, 52, 74, 5, 24, 3], [2, 5, 50, 43, 1120, 52, 74, 654, 3], [2, 13, 79, 17, 60, 325, 4, 325, 310, 28, 842, 1487, 3], [2, 13, 79, 17, 60, 297, 74, 5, 94, 213, 1023, 3], [2, 13, 79, 4, 164, 184, 215, 2939, 701, 3], [2, 13, 520, 79, 60, 36, 1773, 1, 3], [2, 13, 97, 184, 6, 5, 160, 3], [2, 55, 12, 503, 59, 165, 23, 193, 206, 3], [2, 55, 12, 25, 243, 59, 235, 71, 140, 23, 206, 3], [2, 503, 59, 3], [2, 503, 59, 23, 206, 3], [2, 503, 50, 59, 49, 23, 1322, 3], [2, 13, 27, 8, 14, 31, 1227, 93, 554, 8, 202, 33, 174, 3], [2, 13, 37, 31, 90, 87, 12, 93, 1514, 3], [2, 13, 50, 31, 17, 4, 1514, 81, 3], [2, 13, 50, 31, 4, 1514, 3], [2, 13, 14, 8, 27, 31, 52, 254, 174, 3], [2, 16, 535, 36, 28, 527, 186, 3], [2, 16, 4, 26, 442, 36, 28, 527, 204, 3], [2, 16, 7, 364, 36, 4, 26, 38, 3], [2, 43, 309, 36, 28, 527, 186, 3], [2, 25, 16, 364, 36, 28, 527, 3], [2, 80, 27, 8, 14, 9, 265, 15, 8, 14, 9, 33, 5, 41, 3], [2, 5, 13, 31, 17, 32, 33, 5, 41, 3], [2, 13, 1, 1, 32, 33, 5, 41, 3], [2, 13, 31, 17, 32, 33, 57, 252, 3], [2, 13, 31, 17, 32, 33, 5, 41, 66, 344, 8, 205, 3], [2, 19, 4, 91, 21, 556, 8, 293, 56, 92, 59, 3], [2, 19, 4, 57, 556, 4, 91, 10, 47, 92, 59, 3], [2, 26, 322, 19, 315, 1003, 218, 7, 21, 1071, 57, 260, 8, 1718, 8, 34, 4, 5, 91, 10, 92, 167, 3], [2, 1018, 19, 2591, 5, 1003, 218, 76, 443, 397, 4, 5, 91, 10, 1718, 3], [2, 25, 19, 10, 556, 8, 1718, 321, 71, 5, 94, 76, 443, 111, 4, 91, 3], [2, 15, 9, 110, 3967, 4, 29, 68, 3], [2, 15, 9, 110, 3967, 4, 29, 68, 90, 23, 5, 42, 21, 30, 3], [2, 15, 9, 53, 1, 4, 75, 68, 78, 85, 20, 42, 3], [2, 15, 9, 123, 50, 14, 3967, 4, 75, 68, 3], [2, 5, 15, 9, 110, 105, 4, 75, 68, 8, 42, 78, 320, 3], [2, 11, 224, 848, 6, 29, 139, 6, 5, 160, 3], [2, 11, 7, 136, 848, 6, 107, 77, 3], [2, 11, 6, 139, 7, 6, 1225, 5, 162, 617, 3], [2, 243, 11, 4, 89, 98, 7, 2035, 848, 6, 29, 77, 3], [2, 11, 6, 139, 73, 6, 1225, 80, 617, 3], [2, 55, 7, 49, 99, 142, 3137, 3], [2, 55, 12, 22, 165, 860, 142, 107, 3], [2, 55, 12, 22, 165, 4, 5, 44, 410, 107, 738, 3], [2, 292, 59, 361, 359, 20, 368, 3], [2, 292, 22, 17, 49, 141, 4, 5, 44, 3], [2, 219, 257, 78, 1, 6, 64, 35, 330, 1897, 4, 3], [2, 54, 257, 78, 4, 5, 24, 6, 5, 64, 3], [2, 324, 257, 7, 39, 6, 5, 127, 209, 5, 125, 3], [2, 24, 257, 39, 23, 5, 125, 281, 3], [2, 14, 1507, 78, 324, 76, 154, 116, 87, 2646, 5, 125, 3], [2, 42, 78, 66, 396, 1504, 3], [2, 18, 104, 1, 108, 7, 906, 3], [2, 18, 39, 66, 1011, 108, 3079, 3], [2, 5, 1504, 17, 187, 4, 699, 8, 1, 3], [2, 18, 906, 6, 2009, 4, 347, 10, 699, 3], [2, 11, 4, 88, 38, 246, 82, 128, 3], [2, 11, 7, 82, 115, 194, 4, 5, 65, 3], [2, 42, 4, 26, 38, 115, 52, 82, 128, 187, 4, 3471, 1830, 3], [2, 82, 350, 4, 26, 38, 3], [2, 82, 350, 1806, 6, 82, 115, 108, 3]]
print(tokenizer.oov_token)
print(tokenizer.index_word[0])
UNK PAD
tokenizer.index_word
{1: 'UNK',
2: '<start>',
3: '<end>',
4: 'in',
5: 'the',
6: 'on',
7: 'is',
8: 'and',
9: 'dog',
10: 'with',
11: 'man',
12: 'of',
13: 'two',
14: 'white',
15: 'black',
16: 'boy',
17: 'are',
18: 'woman',
19: 'girl',
20: 'to',
21: 'wearing',
22: 'people',
23: 'at',
24: 'water',
25: 'young',
26: 'red',
27: 'brown',
28: 'an',
29: 'his',
30: 'blue',
31: 'dogs',
32: 'running',
33: 'through',
34: 'playing',
35: 'while',
36: 'down',
37: 'little',
38: 'shirt',
39: 'standing',
40: 'ball',
41: 'grass',
42: 'person',
43: 'child',
44: 'snow',
45: 'jumping',
46: 'over',
47: 'three',
48: 'front',
49: 'sitting',
50: 'small',
51: 'field',
52: 'up',
53: 'holding',
54: 'large',
55: 'group',
56: 'by',
57: 'green',
58: 'yellow',
59: 'children',
60: 'walking',
61: 'men',
62: 'her',
63: 'into',
64: 'beach',
65: 'air',
66: 'near',
67: 'jumps',
68: 'mouth',
69: 'street',
70: 'runs',
71: 'for',
72: 'another',
73: 'riding',
74: 'from',
75: 'its',
76: 'as',
77: 'bike',
78: 'stands',
79: 'girls',
80: 'one',
81: 'outside',
82: 'rock',
83: 'player',
84: 'off',
85: 'next',
86: 'play',
87: 'out',
88: 'pink',
89: 'orange',
90: 'looking',
91: 'pool',
92: 'other',
93: 'their',
94: 'camera',
95: 'boys',
96: 'jacket',
97: 'women',
98: 'hat',
99: 'around',
100: 'some',
101: 'dirt',
102: 'soccer',
103: 'sits',
104: 'behind',
105: 'toy',
106: 'dressed',
107: 'mountain',
108: 'wall',
109: 'walks',
110: 'has',
111: 'plays',
112: 'along',
113: 'stand',
114: 'crowd',
115: 'climbing',
116: 'looks',
117: 'park',
118: 'four',
119: 'building',
120: 'football',
121: 'across',
122: 'top',
123: 'holds',
124: 'rides',
125: 'ocean',
126: 'grassy',
127: 'sand',
128: 'face',
129: 'background',
130: 'smiling',
131: 'stick',
132: 'swimming',
133: 'hill',
134: 'skateboard',
135: 'car',
136: 'doing',
137: 'baby',
138: 'tennis',
139: 'bicycle',
140: 'picture',
141: 'together',
142: 'snowy',
143: 'each',
144: 'carrying',
145: 'jump',
146: 'tree',
147: 'basketball',
148: 'that',
149: 'hair',
150: 'tan',
151: 'road',
152: 'trick',
153: 'race',
154: 'it',
155: 'area',
156: 'swing',
157: 'bench',
158: 'game',
159: 'head',
160: 'sidewalk',
161: 'catch',
162: 'back',
163: 'him',
164: 'shorts',
165: 'sit',
166: 'run',
167: 'kids',
168: 'helmet',
169: 'dress',
170: 'ground',
171: 'something',
172: 'being',
173: 'frisbee',
174: 'fence',
175: 'lake',
176: 'hand',
177: 'wave',
178: 'skateboarder',
179: 'path',
180: 'ramp',
181: 'several',
182: 'city',
183: 'purple',
184: 'walk',
185: 'there',
186: 'slide',
187: 'covered',
188: 'side',
189: 'baseball',
190: 'players',
191: 'posing',
192: 'track',
193: 'wooden',
194: 'high',
195: 'long',
196: 'big',
197: 'boat',
198: 'sunglasses',
199: 'watches',
200: 'uniform',
201: 'coat',
202: 'look',
203: 'rocks',
204: 'ride',
205: 'trees',
206: 'table',
207: 'motorcycle',
208: 'couple',
209: 'beside',
210: 'snowboarder',
211: 'rope',
212: 'watching',
213: 'under',
214: 'suit',
215: 'towards',
216: 'pants',
217: 'dark',
218: 'sign',
219: 'grey',
220: 'rocky',
221: 'hands',
222: 'river',
223: 'horse',
224: 'does',
225: 'arms',
226: 'racing',
227: 'lady',
228: 'watch',
229: 'ice',
230: 'them',
231: 'older',
232: 'above',
233: 'colorful',
234: 'woods',
235: 'pose',
236: 'jeans',
237: 'guy',
238: 'who',
239: 'midair',
240: 'onto',
241: 'he',
242: 'striped',
243: 'asian',
244: 'leaps',
245: 'playground',
246: 'climbs',
247: 'hockey',
248: 'performing',
249: 'taking',
250: 'cliff',
251: 'mountains',
252: 'yard',
253: 'collar',
254: 'against',
255: 'blonde',
256: 'surfer',
257: 'bird',
258: 'body',
259: 'blond',
260: 'glasses',
261: 'smiles',
262: 'laying',
263: 'kid',
264: 'rider',
265: 'chasing',
266: 'skier',
267: 'many',
268: 'team',
269: 'fountain',
270: 'after',
271: 'during',
272: 'biker',
273: 'wet',
274: 'old',
275: 'cap',
276: 'inside',
277: 'toddler',
278: 'takes',
279: 'open',
280: 'brick',
281: 'edge',
282: 'flying',
283: 'very',
284: 'colored',
285: 'someone',
286: 'guitar',
287: 'shore',
288: 'trying',
289: 'outdoors',
290: 'outfit',
291: 'forest',
292: 'five',
293: 'surrounded',
294: 'middle',
295: 'hanging',
296: 'night',
297: 'away',
298: 'bed',
299: 'light',
300: 'talking',
301: 'pole',
302: 'whilst',
303: 'others',
304: 'object',
305: 'floor',
306: 'tshirt',
307: 'surfboard',
308: 'steps',
309: 'going',
310: 'past',
311: 'line',
312: 'backpack',
313: 'toward',
314: 'about',
315: 'making',
316: 'trail',
317: 'swinging',
318: 'this',
319: 'eating',
320: 'nearby',
321: 'poses',
322: 'haired',
323: 'gray',
324: 'tall',
325: 'arm',
326: 'dancing',
327: 'flowers',
328: 'bridge',
329: 'day',
330: 'waves',
331: 'leaves',
332: 'clothes',
333: 'course',
334: 'fighting',
335: 'leaping',
336: 'sky',
337: 'board',
338: 'shallow',
339: 'window',
340: 'costume',
341: 'outdoor',
342: 'plastic',
343: 'carries',
344: 'house',
345: 'leash',
346: 'splashing',
347: 'room',
348: 'chair',
349: 'legs',
350: 'climber',
351: 'bag',
352: 'stone',
353: 'catches',
354: 'obstacle',
355: 'clothing',
356: 'all',
357: 'bright',
358: 'swings',
359: 'ready',
360: 'between',
361: 'getting',
362: 'skateboarding',
363: 'they',
364: 'sliding',
365: 'bathing',
366: 'sweater',
367: 'trampoline',
368: 'sled',
369: 'gear',
370: 'concrete',
371: 'adult',
372: 'railing',
373: 'wears',
374: 'mud',
375: 'waiting',
376: 'skiing',
377: 'stream',
378: 'metal',
379: 'lawn',
380: 'male',
381: 'winter',
382: 'stairs',
383: 'fire',
384: 'pulling',
385: 'uniforms',
386: 'shirts',
387: 'sandy',
388: 'catching',
389: 'gets',
390: 'set',
391: 'store',
392: 'jersey',
393: 'tries',
394: 'train',
395: 'lot',
396: 'golden',
397: 'swims',
398: 'bar',
399: 'adults',
400: 'fishing',
401: 'rail',
402: 'drink',
403: 'tricks',
404: 'shirtless',
405: 'get',
406: 'flies',
407: 'female',
408: 'smile',
409: 'distance',
410: 'overlooking',
411: 'couch',
412: 'busy',
413: 'tongue',
414: 'sun',
415: 'makes',
416: 'chases',
417: 'performs',
418: 'throwing',
419: 'surfing',
420: 'trunks',
421: 'ski',
422: 'lying',
423: 'number',
424: 'puppy',
425: 'puddle',
426: 'dock',
427: 'huge',
428: 'pond',
429: 'swim',
430: 'drinking',
431: 'wooded',
432: 'animal',
433: 'vest',
434: 'flag',
435: 'tire',
436: 'horses',
437: 'bubbles',
438: 'upside',
439: 'photo',
440: 'leaning',
441: 'cellphone',
442: 'slides',
443: 'she',
444: 'kayak',
445: 'eyes',
446: 'snowboard',
447: 'truck',
448: 'flip',
449: 'laughing',
450: 'nose',
451: 'sunset',
452: 'shopping',
453: 'waterfall',
454: 'right',
455: 'american',
456: 'equipment',
457: 'reading',
458: 'view',
459: 'wetsuit',
460: 'family',
461: 'hold',
462: 'bat',
463: 'greyhound',
464: 'stunt',
465: 'food',
466: 'bikes',
467: 'elderly',
468: 'hats',
469: 'climb',
470: 'restaurant',
471: 'lone',
472: 'life',
473: 'or',
474: 'coming',
475: 'hurdle',
476: 'skis',
477: 'goal',
478: 'shoes',
479: 'like',
480: 'hiker',
481: 'harness',
482: 'left',
483: 'deep',
484: 'skating',
485: 'feet',
486: 'tent',
487: 'biting',
488: 'cart',
489: 'scarf',
490: 'setting',
491: 'no',
492: 'dry',
493: 'mask',
494: 'muddy',
495: 'guys',
496: 'take',
497: 'bmx',
498: 'closeup',
499: 'court',
500: 'fight',
501: 'vehicle',
502: 'umbrella',
503: 'six',
504: 'crowded',
505: 'sweatshirt',
506: 'pile',
507: 'skate',
508: 'dresses',
509: 'raft',
510: 'falling',
511: 'bus',
512: 'german',
513: 'structure',
514: 'ledge',
515: 'have',
516: 'diving',
517: 'tank',
518: 'driving',
519: 'surf',
520: 'teenage',
521: 'paper',
522: 'cyclist',
523: 'slope',
524: 'cement',
525: 'bags',
526: 'canoe',
527: 'inflatable',
528: 'airborne',
529: 'flags',
530: 'be',
531: 'cigarette',
532: 'book',
533: 'subway',
534: 'skirt',
535: 'goes',
536: 'splashes',
537: 'turn',
538: 'wood',
539: 'kick',
540: 'costumes',
541: 'shepherd',
542: 'fallen',
543: 'jackets',
544: 'shaking',
545: 'dance',
546: 'kicking',
547: 'bottle',
548: 'buildings',
549: 'both',
550: 'parking',
551: 'faces',
552: 'tunnel',
553: 'sports',
554: 'ears',
555: 'gathered',
556: 'goggles',
557: 'band',
558: 'hit',
559: 'balls',
560: 'microphone',
561: 'ring',
562: 'bikini',
563: 'parade',
564: 'chairs',
565: 'throws',
566: 'kicks',
567: 'pictures',
568: 'piece',
569: 'bicyclist',
570: 'full',
571: 'hiking',
572: 'beard',
573: 'stage',
574: 'bull',
575: 'rugby',
576: 'painted',
577: 'backyard',
578: 'smaller',
579: 'blanket',
580: 'silver',
581: 'outfits',
582: 'event',
583: 'blowing',
584: 'cars',
585: 'sunny',
586: 'steep',
587: 'swimsuit',
588: 'cat',
589: 'box',
590: 'leather',
591: 'surface',
592: 'statue',
593: 'low',
594: 'drinks',
595: 'scooter',
596: 'short',
597: 'glass',
598: 'sticks',
599: 'stuffed',
600: 'bald',
601: 'suits',
602: 'held',
603: 'volleyball',
604: 'graffiti',
605: 'smoking',
606: 'sleeping',
607: 'pushing',
608: 'using',
609: 'wrestling',
610: 'throw',
611: 'pointing',
612: 'cow',
613: 'staring',
614: 'paint',
615: 'net',
616: 'leans',
617: 'wheel',
618: 'crossing',
619: 'hugging',
620: 'corner',
621: 'fluffy',
622: 'tube',
623: 'door',
624: 'attempting',
625: 'few',
626: 'bucket',
627: 'hind',
628: 'motorcyclist',
629: 'lays',
630: 'make',
631: 'party',
632: 'boots',
633: 'wrestle',
634: 'flower',
635: 'hangs',
636: 'puppies',
637: 'show',
638: 'bunch',
639: 'same',
640: 'police',
641: 'snowboarding',
642: 'cowboy',
643: 'log',
644: 'beautiful',
645: 'wading',
646: 'cup',
647: 'teams',
648: 'sheep',
649: 'leg',
650: 'competition',
651: 'sprinkler',
652: 'eats',
653: 'fast',
654: 'underwater',
655: 'bicycles',
656: 'furry',
657: 'attempts',
658: 'spectators',
659: 'softball',
660: 'gather',
661: 'racetrack',
662: 'garden',
663: 'attached',
664: 'beige',
665: 'dances',
666: 'fish',
667: 'gym',
668: 'go',
669: 'kissing',
670: 'hoop',
671: 'desert',
672: 'end',
673: 'motocross',
674: 'racer',
675: 'phone',
676: 'poles',
677: 'shot',
678: 'racket',
679: 'below',
680: 'seat',
681: 'sticking',
682: 'handstand',
683: 'clear',
684: 'pushes',
685: 'rolling',
686: 'points',
687: 'way',
688: 'prepares',
689: 'platform',
690: 'toys',
691: 'empty',
692: 'onlookers',
693: 'hits',
694: 'wide',
695: 'talks',
696: 'rain',
697: 'gravel',
698: 'shop',
699: 'gold',
700: 'do',
701: 'signs',
702: 'bride',
703: 'paddling',
704: 'waving',
705: 'snowcovered',
706: 'heads',
707: 'public',
708: 'naked',
709: 'wear',
710: 'rough',
711: 'just',
712: 'ladies',
713: 'among',
714: 'base',
715: 'mohawk',
716: 'market',
717: 'headband',
718: 'birds',
719: 'pavement',
720: 'facing',
721: 'reads',
722: 'having',
723: 'traffic',
724: 'plaid',
725: 'branch',
726: 'neck',
727: 'downhill',
728: 'skateboards',
729: 'cream',
730: 'african',
731: 'hose',
732: 'thrown',
733: 'cross',
734: 'drives',
735: 'dirty',
736: 'bars',
737: 'wings',
738: 'scene',
739: 'bottom',
740: 'fenced',
741: 'races',
742: 'different',
743: 'painting',
744: 'lies',
745: 'beer',
746: 'wait',
747: 'indoor',
748: 'barefoot',
749: 'chewing',
750: 'cricket',
751: 'goalie',
752: 'resting',
753: 'splash',
754: 'carpet',
755: 'says',
756: 'waits',
757: 'fall',
758: 'bearded',
759: 'carnival',
760: 'kitchen',
761: 'school',
762: 'rapids',
763: 'blows',
764: 'wedding',
765: 'wrestler',
766: 'match',
767: 'paved',
768: 'balloon',
769: 'lit',
770: 'rural',
771: 'creek',
772: 'teeth',
773: 'onstage',
774: 'stadium',
775: 'opposing',
776: 'moving',
777: 'paddle',
778: 'mother',
779: 'display',
780: 'lap',
781: 'younger',
782: 'filled',
783: 'greyhounds',
784: 'santa',
785: 'smoke',
786: 'hula',
787: 'landscape',
788: 'atv',
789: 'gun',
790: 'video',
791: 'pulled',
792: 'animals',
793: 'motorbike',
794: 'talk',
795: 'shoulder',
796: 'showing',
797: 'before',
798: 'skates',
799: 'falls',
800: 'shooting',
801: 'art',
802: 'boats',
803: 'foot',
804: 'plants',
805: 'headphones',
806: 'chase',
807: 'matching',
808: 'reaching',
809: 'block',
810: 'gloves',
811: 'hay',
812: 'staircase',
813: 'can',
814: 'ropes',
815: 'floating',
816: 'deck',
817: 'where',
818: 'mouths',
819: 'fair',
820: 'try',
821: 'unicycle',
822: 'reaches',
823: 'pipe',
824: 'wire',
825: 'seated',
826: 'retriever',
827: 'urban',
828: 'parked',
829: 'pack',
830: 'martial',
831: 'close',
832: 'instruments',
833: 'singing',
834: 'bubble',
835: 'runner',
836: 'teenagers',
837: 'christmas',
838: 'spotted',
839: 'shakes',
840: 'skiers',
841: 'audience',
842: 'amusement',
843: 'poodle',
844: 'formation',
845: 'bandanna',
846: 'pulls',
847: 'multicolored',
848: 'wheelie',
849: 'digging',
850: 'lights',
851: 'surfs',
852: 'grinding',
853: 'flight',
854: 'duck',
855: 'hole',
856: 'outstretched',
857: 'lined',
858: 'fly',
859: 'hikers',
860: 'atop',
861: 'helmets',
862: 'hitting',
863: 'purse',
864: 'follows',
865: 'funny',
866: 'shoulders',
867: 'kite',
868: 'covering',
869: 'raises',
870: 'perform',
871: 'giant',
872: 'row',
873: 'newspaper',
874: 'puts',
875: 'spray',
876: 'alone',
877: 'stop',
878: 'preparing',
879: 'bank',
880: 'photograph',
881: 'ear',
882: 'enjoys',
883: 'bushes',
884: 'hoodie',
885: 'balancing',
886: 'curly',
887: 'muzzled',
888: 'rodeo',
889: 'jumped',
890: 'rowing',
891: 'kneeling',
892: 'paddles',
893: 'pier',
894: 'alongside',
895: 'backwards',
896: 'music',
897: 'jeep',
898: 'parachute',
899: 'muzzle',
900: 'putting',
901: 'hang',
902: 'pull',
903: 'laughs',
904: 'station',
905: 'giving',
906: 'writing',
907: 'gives',
908: 'cold',
909: 'tie',
910: 'denim',
911: 'owner',
912: 'indoors',
913: 'spinning',
914: 'plate',
915: 'himself',
916: 'gate',
917: 'bite',
918: 'chain',
919: 'riders',
920: 'bites',
921: 'break',
922: 'appears',
923: 'safety',
924: 'handrail',
925: 'playfully',
926: 'fetch',
927: 'finger',
928: 'brightly',
929: 'bikers',
930: 'picnic',
931: 'makeup',
932: 'underneath',
933: 'competing',
934: 'mound',
935: 'licking',
936: 'tackle',
937: 'terrier',
938: 'seven',
939: 'rink',
940: 'construction',
941: 'paws',
942: 'shows',
943: 'machine',
944: 'collie',
945: 'these',
946: 'shaggy',
947: 'foreground',
948: 'ladder',
949: 'hillside',
950: 'referee',
951: 'enjoying',
952: 'roller',
953: 'necklace',
954: 'backs',
955: 'larger',
956: 'professional',
957: 'doorway',
958: 'passing',
959: 'bowl',
960: 'waters',
961: 'kayaking',
962: 'terrain',
963: 'quickly',
964: 'helps',
965: 'half',
966: 'tug',
967: 'backpacks',
968: 'step',
969: 'hooded',
970: 'float',
971: 'splashed',
972: 'jungle',
973: 'taken',
974: 'balances',
975: 'made',
976: 'lean',
977: 'skater',
978: 'third',
979: 'counter',
980: 'homeless',
981: 'sniffing',
982: 'shadow',
983: 'hoops',
984: 'caught',
985: 'friend',
986: 'pigeons',
987: 'violin',
988: 'fingers',
989: 'military',
990: 'wheelchair',
991: 'basket',
992: 'round',
993: 'spots',
994: 'courtyard',
995: 'crouches',
996: 'streets',
997: 'bouncing',
998: 'biking',
999: 'leap',
1000: 'straw',
...}
tokenizer.word_counts
OrderedDict([('<start>', 40455),
('child', 1517),
('in', 18182),
('pink', 702),
('dress', 332),
('is', 9069),
('climbing', 490),
('up', 1215),
('set', 105),
('of', 6496),
('stairs', 109),
('an', 2358),
('entry', 1),
('way', 48),
('<end>', 40455),
('girl', 3284),
('going', 145),
('into', 1046),
('wooden', 278),
('building', 485),
('little', 1750),
('playhouse', 6),
('the', 17676),
('to', 3005),
('her', 1102),
('cabin', 4),
('black', 3627),
('dog', 7956),
('and', 8057),
('spotted', 36),
('are', 3365),
('fighting', 130),
('tricolored', 12),
('playing', 1954),
('with', 7308),
('each', 399),
('other', 667),
('on', 10367),
('road', 380),
('white', 3710),
('brown', 2471),
('spots', 28),
('staring', 57),
('at', 2819),
('street', 909),
('two', 5546),
('dogs', 2097),
('different', 43),
('breeds', 5),
('looking', 682),
('pavement', 46),
('moving', 40),
('toward', 142),
('covered', 286),
('paint', 57),
('sits', 561),
('front', 1322),
('painted', 62),
('rainbow', 17),
('hands', 231),
('bowl', 29),
('sitting', 1322),
('large', 1209),
('small', 1264),
('grass', 1565),
('plays', 515),
('fingerpaints', 3),
('canvas', 6),
('it', 370),
('there', 296),
('pigtails', 14),
('painting', 43),
('young', 2610),
('outside', 757),
('man', 7164),
('lays', 55),
('bench', 360),
('while', 1818),
('his', 2255),
('by', 1167),
('him', 353),
('which', 27),
('also', 15),
('tied', 13),
('sleeping', 58),
('next', 714),
('shirtless', 100),
('lies', 43),
('park', 487),
('laying', 182),
('holding', 1211),
('leash', 125),
('ground', 332),
('orange', 696),
('hat', 633),
('starring', 8),
('something', 329),
('wears', 111),
('glasses', 184),
('gauges', 2),
('wearing', 2917),
('blitz', 1),
('beer', 43),
('can', 37),
('crocheted', 1),
('pierced', 6),
('ears', 65),
('rope', 237),
('net', 57),
('red', 2553),
('roping', 2),
('climbs', 197),
('bridge', 135),
('grips', 2),
('onto', 204),
('ropes', 37),
('playground', 198),
('running', 1996),
('grassy', 446),
('garden', 51),
('surrounded', 155),
('fence', 319),
('through', 1979),
('boston', 9),
('terrier', 30),
('lush', 8),
('green', 1157),
('runs', 906),
('near', 962),
('shakes', 36),
('its', 863),
('head', 358),
('shore', 157),
('edge', 165),
('beach', 1018),
('ball', 1678),
('feet', 79),
('water', 2676),
('standing', 1703),
('turned', 17),
('one', 768),
('side', 286),
('boy', 3534),
('smiles', 183),
('stony', 3),
('wall', 533),
('city', 309),
('overalls', 23),
('working', 23),
('stone', 123),
('aross', 1),
('walking', 1126),
('paved', 41),
('metal', 110),
('pole', 151),
('behind', 553),
('smiling', 437),
('shirt', 1735),
('blue', 2125),
('jeans', 207),
('rock', 730),
('leaps', 200),
('over', 1378),
('log', 53),
('grey', 232),
('leaping', 129),
('fallen', 67),
('tree', 391),
('mottled', 2),
('collar', 193),
('jumping', 1431),
('jumped', 33),
('stump', 27),
('snow', 1439),
('field', 1221),
('surface', 60),
('displaying', 5),
('pictures', 63),
('skier', 179),
('skis', 81),
('past', 145),
('another', 893),
('paintings', 4),
('person', 1521),
('framed', 7),
('looks', 490),
('trees', 247),
('artwork', 4),
('for', 901),
('sale', 7),
('collage', 2),
('cliff', 194),
('group', 1207),
('people', 2837),
('belays', 1),
('seven', 30),
('climbers', 27),
('ascending', 8),
('face', 445),
('whilst', 147),
('stands', 841),
('several', 310),
('row', 34),
('watches', 259),
('holds', 453),
('line', 145),
('chases', 97),
('from', 866),
('sprinkler', 52),
('lawn', 110),
('hose', 44),
('away', 153),
('prepares', 48),
('catch', 354),
('thrown', 44),
('object', 146),
('nearby', 140),
('cars', 61),
('about', 142),
('yellow', 1149),
('mouth', 913),
('toy', 549),
('ready', 119),
('flying', 165),
('air', 1008),
('after', 176),
('get', 100),
('jumps', 955),
('towards', 234),
('trying', 157),
('midair', 205),
('woman', 3320),
('waters', 29),
('big', 267),
('lake', 318),
('lone', 82),
('duck', 35),
('swimming', 433),
('around', 622),
('watching', 237),
('waves', 134),
('hand', 318),
('couple', 244),
('infant', 15),
('sit', 349),
('baby', 411),
('stroller', 26),
('their', 662),
('newborn', 8),
('under', 237),
('facing', 46),
('care', 3),
('along', 508),
('body', 185),
('outdoors', 156),
('surf', 72),
('lab', 24),
('tags', 7),
('frolicks', 2),
('splashes', 69),
('this', 141),
('splashing', 125),
('drilling', 5),
('hole', 35),
('ice', 218),
('frozen', 25),
('pond', 92),
('men', 1105),
('fishing', 102),
('play', 713),
('making', 142),
('turn', 69),
('soft', 13),
('sand', 446),
('together', 403),
('tan', 381),
('sandy', 106),
('uses', 26),
('picks', 12),
('crampons', 1),
('scale', 1),
('climber', 124),
('jacket', 649),
('pants', 234),
('scaling', 20),
('waterfall', 85),
('carries', 126),
('as', 843),
('he', 203),
('walks', 531),
('carrying', 396),
('has', 519),
('item', 18),
('wet', 170),
('kayak', 87),
('life', 82),
('jackets', 67),
('rowing', 33),
('canoe', 71),
('gentle', 1),
('ride', 248),
('courtyard', 28),
('catching', 106),
('snaps', 1),
('lunges', 6),
('chocolate', 13),
('too', 6),
('late', 3),
('captures', 1),
('driveway', 13),
('stick', 437),
('kneeling', 33),
('goalie', 42),
('hockey', 195),
('guarding', 9),
('goal', 81),
('kid', 182),
('rink', 30),
('male', 110),
('right', 85),
('crouches', 28),
('modern', 5),
('art', 38),
('structure', 73),
('glass', 59),
('reads', 46),
('newspaper', 34),
('sculpture', 22),
('office', 11),
('statue', 60),
('backpack', 145),
('buildings', 66),
('reading', 84),
('tent', 79),
('being', 326),
('enter', 8),
('setting', 78),
('hut', 12),
('iced', 3),
('snowy', 402),
('three', 1366),
('hill', 429),
('sky', 129),
('them', 216),
('stand', 498),
('kneels', 26),
('skyscraper', 6),
('very', 159),
('tall', 139),
('distance', 99),
('camera', 658),
('bites', 31),
('hard', 16),
('treat', 16),
('biting', 79),
('baked', 2),
('good', 7),
('putting', 32),
('both', 66),
('eats', 52),
('food', 83),
('table', 246),
('eating', 141),
('pizza', 10),
('tin', 1),
('dish', 10),
('mountainside', 25),
('check', 6),
('out', 705),
('view', 84),
('hilltop', 2),
('overlooking', 99),
('valley', 21),
('hang', 32),
('top', 463),
('overlook', 6),
('rest', 24),
('ledge', 73),
('above', 212),
('moutains', 1),
('down', 1755),
('many', 179),
('inflatable', 71),
('boats', 38),
('kayakers', 8),
('railing', 112),
('rafts', 6),
('below', 49),
('crowd', 494),
('jersey', 104),
('pose', 208),
('some', 582),
('multiracial', 1),
('posing', 281),
('picture', 407),
('asian', 202),
('blond', 185),
('guy', 207),
('striped', 203),
('takeout', 1),
('television', 9),
('floor', 146),
('fast', 52),
('meal', 13),
('someone', 158),
('tv', 9),
('teens', 7),
('rail', 101),
('crowded', 75),
('takes', 166),
('jump', 394),
('skateboard', 426),
('performing', 195),
('trick', 375),
('leans', 57),
('skateboarder', 313),
('doing', 417),
('board', 129),
('platform', 48),
('skateboarders', 11),
('paddling', 47),
('river', 229),
('kayaking', 29),
('paddles', 33),
('boat', 265),
('paddle', 40),
('shallow', 128),
('girls', 837),
('ocean', 447),
('four', 487),
('children', 1143),
('pajamas', 18),
('have', 73),
('pillow', 15),
('fight', 76),
('kids', 338),
('pillowfight', 1),
('bed', 153),
('having', 46),
('constructions', 1),
('workers', 18),
('beam', 20),
('taking', 195),
('break', 31),
('construction', 30),
('ibeam', 1),
('take', 77),
('seat', 49),
('steel', 4),
('boys', 652),
('puddle', 93),
('balloon', 41),
('mud', 111),
('sunny', 61),
('day', 135),
('appears', 31),
('wait', 43),
('hailing', 1),
('taxi', 4),
('signaling', 1),
('traffic', 46),
('blonde', 191),
('hair', 382),
('tube', 56),
('waving', 47),
('arm', 138),
('oncoming', 1),
('brochure', 2),
('train', 103),
('rides', 451),
('magizine', 1),
('book', 70),
('pamphlet', 1),
('rocky', 232),
('run', 346),
('across', 465),
('stones', 9),
('area', 362),
('background', 442),
('descends', 11),
('off', 720),
('end', 50),
('high', 273),
('diving', 73),
('pool', 679),
('dive', 11),
('window', 128),
('overshirt', 1),
('tank', 73),
('chrome', 1),
('door', 56),
('puts', 34),
('elevator', 6),
('lightcolored', 4),
('light', 152),
('swim', 92),
('shorts', 352),
('trunks', 95),
('arms', 223),
('outstretched', 35),
('hiker', 80),
('bluff', 5),
('mountains', 194),
('ski', 95),
('landscape', 39),
('snowcovered', 47),
('mountain', 536),
('beautiful', 53),
('pauses', 10),
('mountaintop', 25),
('attempting', 56),
('purple', 301),
('low', 60),
('cut', 26),
('yard', 194),
('frisbee', 323),
('parking', 66),
('lot', 103),
('middle', 155),
('during', 171),
('heavy', 25),
('mat', 22),
('between', 119),
('suv', 13),
('pickup', 4),
('open', 166),
('busy', 98),
('terrain', 29),
('woolly', 1),
('doberman', 17),
('chasing', 181),
('catches', 123),
('tennis', 409),
('multicolor', 12),
('balloons', 25),
('night', 154),
('hot', 24),
('lit', 41),
('lined', 35),
('nighttime', 13),
('helmet', 334),
('bike', 843),
('miniature', 5),
('dirt', 577),
('bicycle', 409),
('race', 375),
('pedals', 3),
('quickly', 29),
('bmx', 77),
('eight', 15),
('gathered', 65),
('dark', 234),
('porch', 24),
('darkened', 10),
('room', 125),
('throwing', 96),
('cleans', 2),
('bubbles', 89),
('suds', 4),
('wiped', 2),
('clean', 7),
('foam', 19),
('ramp', 311),
('soapy', 1),
('getting', 119),
('cleaned', 1),
('slides', 88),
('slide', 294),
('wading', 53),
('toys', 48),
('floating', 37),
('backyard', 62),
('sliding', 117),
('colorful', 210),
('tubes', 2),
('falling', 74),
('colored', 159),
('wetsuit', 84),
('toddler', 167),
('waiting', 111),
('come', 11),
('so', 14),
('droplets', 6),
('fly', 35),
('sticks', 59),
('tongue', 98),
('make', 55),
('faces', 66),
('look', 250),
('silly', 15),
('horse', 229),
('sweatshirt', 75),
('fire', 108),
('barrel', 22),
('lead', 16),
('horses', 90),
('bulldog', 9),
('pushing', 58),
('anouther', 1),
('skinny', 18),
('smaller', 62),
('int', 6),
('various', 19),
('sizes', 4),
('lady', 220),
('no', 78),
('dock', 93),
('deck', 37),
('closeup', 77),
('that', 386),
('paws', 30),
('lying', 95),
('resting', 42),
('tiled', 9),
('eyes', 87),
('rests', 23),
('patio', 20),
('bricks', 4),
('artificial', 9),
('safety', 31),
('harness', 80),
('indoor', 43),
('ring', 64),
('jumphouse', 1),
('teenage', 72),
('seating', 6),
('inflated', 5),
('family', 84),
('tractor', 9),
('polaris', 3),
('vehicle', 76),
('played', 18),
('wheeler', 12),
('riding', 884),
('atv', 39),
('costume', 128),
('left', 80),
('sequined', 4),
('feather', 10),
('sidewalk', 357),
('salmon', 2),
('bikini', 64),
('outfit', 156),
('drinking', 92),
('pop', 7),
('approached', 2),
('flamboyant', 3),
('dressed', 541),
('feathered', 7),
('headress', 1),
('skiiers', 9),
('forest', 156),
('skiing', 111),
('wooded', 92),
('crosscountry', 20),
('skiers', 36),
('woodland', 21),
('trail', 142),
('woods', 209),
('hikers', 35),
('pathway', 11),
('path', 313),
('happily', 26),
('energetic', 1),
('mother', 40),
('boardwalk', 23),
('sea', 26),
('pier', 33),
('evening', 8),
('pony', 12),
('wintertime', 2),
('atop', 35),
('draft', 3),
('daft', 1),
('pull', 32),
('cart', 79),
('golden', 103),
('sleigh', 3),
('driven', 10),
('coat', 252),
('pulling', 108),
('carriage', 19),
('sled', 114),
('steered', 2),
('sheer', 16),
('using', 58),
('rappels', 2),
('steep', 61),
('incline', 11),
('vest', 91),
('inside', 169),
('dome', 3),
('shaft', 1),
('cave', 20),
('shows', 30),
('spelunkers', 2),
('cavern', 1),
('bathed', 1),
('sunlight', 11),
('rocks', 250),
('backpackers', 2),
('lay', 20),
('dry', 78),
('camp', 6),
('gear', 113),
('chalk', 15),
('portrait', 6),
('stream', 111),
('drawing', 13),
('barnlike', 1),
('elaborate', 11),
('illustration', 2),
('deep', 80),
('pile', 75),
('fountain', 178),
('fountains', 14),
('sprayed', 24),
('corgi', 4),
('tunnel', 66),
('course', 131),
('obstacle', 122),
('obedience', 3),
('swimsuit', 61),
('handrail', 31),
('bathing', 117),
('suit', 237),
('spray', 34),
('soaked', 12),
('jet', 21),
('sticking', 49),
('shower', 20),
('gets', 106),
('underwater', 52),
('redheaded', 27),
('swims', 103),
('gun', 39),
('foot', 38),
('aims', 5),
('fireplace', 10),
('new', 17),
('lightup', 1),
('pointed', 12),
('alone', 34),
('jagged', 7),
('snowmobile', 13),
('mid', 8),
('snowmobiler', 2),
('flies', 100),
('rider', 182),
('or', 82),
('machine', 30),
('pine', 22),
('rural', 41),
('snowmobiles', 4),
('helmets', 35),
('goggles', 65),
('snowmobiling', 2),
('helmeted', 15),
('drive', 16),
('atvs', 3),
('heads', 47),
('wheel', 57),
('wheelers', 3),
('empty', 48),
('all', 121),
('gin', 1),
('fourwheeler', 6),
('airborne', 71),
('quad', 6),
('harvested', 2),
('cornfield', 1),
('happy', 19),
('od', 1),
('playfully', 31),
('soccer', 566),
('tucked', 1),
('tshirt', 146),
('artist', 13),
('paints', 12),
('clouds', 25),
('braids', 6),
('colors', 21),
('paper', 72),
('cyclist', 72),
('curved', 4),
('aerodynamic', 2),
('sharp', 20),
('curve', 24),
('pedaling', 3),
('biker', 171),
('fetch', 31),
('pounces', 7),
('cine', 1),
('old', 170),
('fashioned', 6),
('video', 39),
('steadies', 2),
('aim', 4),
('rosy', 1),
('cheeks', 6),
('lips', 17),
('border', 9),
('collie', 30),
('audience', 36),
('dug', 3),
('agile', 1),
('onlookers', 48),
('closely', 5),
('smooth', 6),
('stacking', 1),
('against', 193),
('backdrop', 9),
('shoes', 81),
('rappeling', 1),
('headlamp', 2),
('attached', 51),
('snakeskin', 1),
('sprays', 11),
('frog', 5),
('public', 47),
('wood', 69),
('barrior', 1),
('animal', 92),
('bird', 186),
('seeds', 6),
('sunflower', 1),
('clinging', 5),
('finger', 31),
('wades', 12),
('guided', 3),
('laughs', 32),
('playful', 7),
('beige', 51),
('torwards', 1),
('outdoor', 128),
('handstand', 49),
('does', 226),
('sheets', 5),
('upside', 89),
('snowboard', 87),
('surfboard', 146),
('bikes', 83),
('traveling', 16),
('worn', 5),
('motorcycles', 27),
('motorbikes', 7),
('follow', 14),
('dirtbikes', 2),
('offroad', 9),
('garment', 1),
('flag', 91),
('crescent', 1),
('moon', 3),
('star', 11),
('gown', 9),
('muslim', 3),
('helps', 29),
('wave', 314),
('half', 29),
('naked', 47),
('chair', 125),
('older', 213),
('back', 354),
('relaxes', 12),
('adobe', 1),
('where', 37),
('bicycles', 52),
('propped', 4),
('cap', 170),
('relaxing', 15),
('folding', 10),
('topless', 19),
('slippers', 2),
('hauling', 1),
('retrieve', 9),
('mouths', 37),
('tug', 29),
('chew', 16),
('party', 55),
('streets', 28),
('drink', 101),
('they', 118),
('women', 641),
('parade', 64),
('neckdeep', 1),
('creating', 8),
('splash', 42),
('seaweed', 10),
('lav', 1),
('sandals', 17),
('pinstripe', 2),
('snows', 4),
('furry', 52),
('attempts', 52),
('itself', 19),
('shaking', 67),
('self', 5),
('backpacks', 29),
('placed', 9),
('cardboard', 24),
('bus', 74),
('station', 32),
('bouncing', 28),
('folded', 8),
('beds', 3),
('bedroom', 5),
('snowboarder', 239),
('slope', 72),
('boarders', 1),
('snowboarders', 12),
('clothing', 122),
('store', 105),
('opening', 13),
('stores', 8),
('piece', 63),
('attire', 25),
('car', 419),
('strip', 22),
('boots', 55),
('stepping', 8),
('van', 16),
('wear', 47),
('game', 359),
('plants', 38),
('crossing', 57),
('greenery', 6),
('suspension', 1),
('tropical', 12),
('caution', 6),
('sign', 234),
('beside', 243),
('bright', 121),
('truck', 87),
('others', 147),
('watch', 220),
('helping', 27),
('step', 29),
('pulled', 39),
('passengers', 7),
('load', 2),
('brightly', 31),
('poses', 140),
('pig', 5),
('hugs', 23),
('who', 206),
('embracing', 8),
('hugging', 57),
('pinkhooded', 1),
('stretch', 11),
('bicyclist', 63),
('cross', 44),
('spandex', 2),
('biking', 28),
('jogging', 25),
('headset', 5),
('walkman', 1),
('jogs', 13),
('headphones', 38),
('plant', 16),
('corner', 57),
('bicyclists', 24),
('intersection', 14),
('bikers', 31),
('stop', 34),
('town', 24),
('without', 25),
('guiding', 5),
('wagon', 25),
('escorts', 1),
('leading', 20),
('horsedrawn', 1),
('shetland', 1),
('hits', 48),
('tee', 6),
('practices', 10),
('hitting', 35),
('baseball', 286),
('bats', 3),
('put', 24),
('batting', 7),
('cage', 14),
('spins', 15),
('sun', 98),
('she', 88),
('trumpet', 8),
('marching', 20),
('band', 65),
('teenager', 25),
('trumped', 1),
('starbucks', 2),
('samples', 3),
('fair', 37),
('apron', 19),
('serving', 4),
('drinks', 60),
('tray', 7),
('barista', 1),
('offering', 6),
('waitress', 2),
('offers', 5),
...])
imageCaptions[:5]
['<start> child in pink dress is climbing up set of stairs in an entry way <end>', '<start> girl going into wooden building <end>', '<start> little girl climbing into wooden playhouse <end>', '<start> little girl climbing the stairs to her playhouse <end>', '<start> little girl in pink dress going into wooden cabin <end>']
train_seqs[:5]
[[2, 43, 4, 88, 169, 7, 115, 52, 390, 12, 382, 4, 28, 1, 687, 3], [2, 19, 309, 63, 193, 119, 3], [2, 37, 19, 115, 63, 193, 2354, 3], [2, 37, 19, 115, 5, 382, 20, 62, 2354, 3], [2, 37, 19, 4, 88, 169, 309, 63, 193, 2889, 3]]
print("Length of sequence:", len(train_seqs))
print(type(train_seqs))
Length of sequence: 40455 <class 'list'>
# Top 20 occuring words after text processing
tokenizer_top_words= [word for line in imageCaptions for word in line.split()]
tokenizer_top_words_count=collections.Counter(tokenizer_top_words)
tokenizer_top_words_count
for word, count in tokenizer_top_words_count.most_common(20):
print(word, ": ", count)
token_lst = tokenizer_top_words_count.most_common(20)
most_common_words_df = pd.DataFrame(token_lst, columns = ['Word', 'Count'])
most_common_words_df.plot.bar(x='Word',y='Count',width=0.6,color='red')
plt.title("Top 20 most occuring words")
plt.xlabel("Words")
plt.ylabel("Counts")
plt.grid(b=None)
<start> : 40455 <end> : 40455 in : 18182 the : 17676 on : 10367 is : 9069 and : 8057 dog : 7956 with : 7308 man : 7164 of : 6496 two : 5546 white : 3710 black : 3627 boy : 3534 are : 3365 woman : 3320 girl : 3284 to : 3005 wearing : 2917
#wordcount from the top frequency words
wordcloud_token = WordCloud(width = 1000, height = 500).generate_from_frequencies(tokenizer_top_words_count)
plt.figure(figsize=(8,8))
plt.imshow(wordcloud_token)
plt.grid(b=None)
train_seqs_len=[len(seq) for seq in train_seqs]
longest_word_length= max(train_seqs_len)
print("The longest word length is:", longest_word_length)
#creating caption vector with the longest word length as the size of caption - padding
cap_vector= tf.keras.preprocessing.sequence.pad_sequences(train_seqs, padding='post',maxlen=longest_word_length,
dtype='int32', value=0)
#padding=post because put the zeroes at the end of sequence - will cause problems while creating embedding vector so we will mask then
#Reason: 0 after <start> tag indicates no word
print("The shape of Caption vector is :" + str(cap_vector.shape))
The longest word length is: 31 The shape of Caption vector is :(40455, 31)
Pre-Processing the images:
#checking to know about image format as we will be setting data format as none in tf.keras.applications.inception_v3.preprocess_input
tf.keras.backend.image_data_format()
'channels_last'
Image data format have default as 'channels_last' which indicates image data is represented in a three-dimensional array where the last channel represents the color channels, e.g. [rows][cols][channels].
IMAGE_SHAPE= (299, 299)
#InceptionV3 does inbuilt preprocessing; so lets do for some images here
preprocessed_img=[]
for img in images[0:5]:
#tensor is a generalization of vectors and matrices to potentially higher dimensions
img = tf.io.read_file(img,name=None) #returns a tensor with contents of i/p file
# by default channels = 0 which indicates to use the number of channels in the JPEG-encoded image.
#Since its RGB images,we can give channels=3 also which will output an RGB image
img = tf.image.decode_jpeg(img,channels=0) #unit8 tensor
img = tf.image.resize(img, IMAGE_SHAPE)
#preprocesses a tensor encoding a batch of images, format = tf.keras.backend.image_data_format()
img = tf.keras.applications.inception_v3.preprocess_input(img,data_format=None)
preprocessed_img.append(img)
Display_Images= preprocessed_img[0:5]
figure, axes = plt.subplots(1,5)
figure.set_figwidth(20)
for ax,image in zip(axes, Display_Images):
print("Shape after resize :", image.shape)
ax.imshow(image)
ax.grid('off')
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Shape after resize : (299, 299, 3) Shape after resize : (299, 299, 3) Shape after resize : (299, 299, 3) Shape after resize : (299, 299, 3) Shape after resize : (299, 299, 3)
#Function to preprocess any image, from 2 blocks above
def preprocess_images_function(image_path):
img = tf.io.read_file(image_path,name=None)
img = tf.image.decode_jpeg(img,channels=0)
img = tf.image.resize(img, IMAGE_SHAPE)
img = tf.keras.applications.inception_v3.preprocess_input(img,data_format=None) #Default value for data format is None
return img, image_path
Creating test and train data
imageLocations
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'Flickr8K/Images/1234817607_924893f6e1.jpg', 'Flickr8K/Images/1234817607_924893f6e1.jpg', 'Flickr8K/Images/1234817607_924893f6e1.jpg', 'Flickr8K/Images/1234817607_924893f6e1.jpg', 'Flickr8K/Images/1235580648_7eebaed9bc.jpg', 'Flickr8K/Images/1235580648_7eebaed9bc.jpg', 'Flickr8K/Images/1235580648_7eebaed9bc.jpg', 'Flickr8K/Images/1235580648_7eebaed9bc.jpg', 'Flickr8K/Images/1235580648_7eebaed9bc.jpg', 'Flickr8K/Images/1235681222_819231767a.jpg', 'Flickr8K/Images/1235681222_819231767a.jpg', 'Flickr8K/Images/1235681222_819231767a.jpg', 'Flickr8K/Images/1235681222_819231767a.jpg', 'Flickr8K/Images/1235681222_819231767a.jpg', 'Flickr8K/Images/1235685934_be89b231fb.jpg', 'Flickr8K/Images/1235685934_be89b231fb.jpg', 'Flickr8K/Images/1235685934_be89b231fb.jpg', 'Flickr8K/Images/1235685934_be89b231fb.jpg', 'Flickr8K/Images/1235685934_be89b231fb.jpg', 'Flickr8K/Images/1236951314_0308dc4138.jpg', 'Flickr8K/Images/1236951314_0308dc4138.jpg', 'Flickr8K/Images/1236951314_0308dc4138.jpg', 'Flickr8K/Images/1236951314_0308dc4138.jpg', 'Flickr8K/Images/1236951314_0308dc4138.jpg', 'Flickr8K/Images/1236964638_1808784a3c.jpg', 'Flickr8K/Images/1236964638_1808784a3c.jpg', 'Flickr8K/Images/1236964638_1808784a3c.jpg', 'Flickr8K/Images/1236964638_1808784a3c.jpg', 'Flickr8K/Images/1236964638_1808784a3c.jpg', 'Flickr8K/Images/1237985362_dbafc59280.jpg', 'Flickr8K/Images/1237985362_dbafc59280.jpg', 'Flickr8K/Images/1237985362_dbafc59280.jpg', 'Flickr8K/Images/1237985362_dbafc59280.jpg', 'Flickr8K/Images/1237985362_dbafc59280.jpg', 'Flickr8K/Images/123889082_d3751e0350.jpg', 'Flickr8K/Images/123889082_d3751e0350.jpg', 'Flickr8K/Images/123889082_d3751e0350.jpg', 'Flickr8K/Images/123889082_d3751e0350.jpg', 'Flickr8K/Images/123889082_d3751e0350.jpg', 'Flickr8K/Images/123997871_6a9ca987b1.jpg', 'Flickr8K/Images/123997871_6a9ca987b1.jpg', 'Flickr8K/Images/123997871_6a9ca987b1.jpg', 'Flickr8K/Images/123997871_6a9ca987b1.jpg', 'Flickr8K/Images/123997871_6a9ca987b1.jpg', 'Flickr8K/Images/1240297429_c36ae0c58f.jpg', 'Flickr8K/Images/1240297429_c36ae0c58f.jpg', 'Flickr8K/Images/1240297429_c36ae0c58f.jpg', 'Flickr8K/Images/1240297429_c36ae0c58f.jpg', 'Flickr8K/Images/1240297429_c36ae0c58f.jpg', 'Flickr8K/Images/124195430_d14028660f.jpg', 'Flickr8K/Images/124195430_d14028660f.jpg', 'Flickr8K/Images/124195430_d14028660f.jpg', 'Flickr8K/Images/124195430_d14028660f.jpg', 'Flickr8K/Images/124195430_d14028660f.jpg', ...]
#preprocess all images
training_list = sorted(set(imageLocations))
#print(training_list)
#slicing dataset
New_Image_Data = tf.data.Dataset.from_tensor_slices(training_list)
#print(New_Image_Data)
#num_parallel_calls=tf.data.experimental.AUTOTUNE - no of parallel executions = amount of cpu available
New_Image_Data = New_Image_Data.map(preprocess_images_function, num_parallel_calls=tf.data.experimental.AUTOTUNE)
# Keeping batch_size=64 (64 consecutive elements of this dataset is combined in a single batch)
# We don't want to drop the last batch if it contains element less than 64; set drop_remainder=False
New_Image_Data= New_Image_Data.batch(64,drop_remainder=False)
New_Image_Data
<BatchDataset shapes: ((None, 299, 299, None), (None,)), types: (tf.float32, tf.string)>
# performing the train-test spliit using 80-20 ratio & random state = 42
img_train_data, img_test_data, caption_train_data, caption_test_data = train_test_split(imageLocations, cap_vector, test_size=0.2, random_state=42)
print("No of images in training dataset: ",len(img_train_data))
img_train_data[:5]
No of images in training dataset: 32364
['Flickr8K/Images/359082432_c1fd5aa2d6.jpg', 'Flickr8K/Images/2460799229_ce45a1d940.jpg', 'Flickr8K/Images/3634828052_3b6aeda7d6.jpg', 'Flickr8K/Images/2216568822_84c295c3b0.jpg', 'Flickr8K/Images/3080056515_3013830309.jpg']
print("No of images in testing dataset: ",len(img_test_data))
img_test_data[0:5]
No of images in testing dataset: 8091
['Flickr8K/Images/2973269132_252bfd0160.jpg', 'Flickr8K/Images/270263570_3160f360d3.jpg', 'Flickr8K/Images/2053006423_6adf69ca67.jpg', 'Flickr8K/Images/512101751_05a6d93e19.jpg', 'Flickr8K/Images/3156406419_38fbd52007.jpg']
print("No of captions in training dataset: ",len(caption_train_data))
caption_train_data[0:5]
No of captions in training dataset: 32364
array([[ 2, 11, 8, 9, 17, 39, 6, 146, 148, 110, 542,
46, 20, 5, 170, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 2, 5, 47, 95, 238, 17, 21, 472, 543, 17, 90,
63, 5, 24, 3, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 2, 18, 10, 94, 10, 283, 195, 2640, 278, 140, 3,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 2, 18, 10, 26, 2236, 168, 8, 1869, 455, 1056, 6,
62, 325, 3, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 2, 80, 42, 7, 3255, 20, 72, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0]])
print("No of captions in testing dataset: ",len(caption_test_data))
caption_test_data[0:5]
No of captions in testing dataset: 8091
array([[ 2, 54, 1626, 588, 7, 4556, 223, 121, 1476, 3, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 2, 13, 27, 31, 500, 6, 5, 1681, 170, 3, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 2, 11, 4, 164, 7, 39, 6, 82, 90, 87, 23,
5, 458, 74, 5, 3975, 3, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 2, 887, 14, 9, 7, 32, 6, 5, 41, 3, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 2, 42, 376, 727, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0]])
Step 1: CNN
#returns a Keras image classification model loaded with weights pre-trained on ImageNet
image_model = tf.keras.applications.InceptionV3(include_top=False,weights='imagenet')
#get i/p and o/p
new_input = image_model.input
hidden_layer = image_model.layers[-1].output
#build the final model using both input & output layer
image_features_extract_model = tf.compat.v1.keras.Model(new_input, hidden_layer)
image_features_extract_model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, None, None, 3 864 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, None, None, 3 96 conv2d[0][0]
__________________________________________________________________________________________________
activation (Activation) (None, None, None, 3 0 batch_normalization[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, None, None, 3 9216 activation[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, None, None, 3 96 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, None, None, 3 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, None, None, 6 18432 activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, None, None, 6 192 conv2d_2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, None, None, 6 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, None, None, 6 0 activation_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, None, None, 8 5120 max_pooling2d[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, None, None, 8 240 conv2d_3[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, None, None, 8 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, None, None, 1 138240 activation_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, None, None, 1 576 conv2d_4[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, None, None, 1 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, None, None, 1 0 activation_4[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, None, None, 6 12288 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, None, None, 6 192 conv2d_8[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, None, None, 6 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, None, None, 4 9216 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, None, None, 9 55296 activation_8[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, None, None, 4 144 conv2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, None, None, 9 288 conv2d_9[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, None, None, 4 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, None, None, 9 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, None, None, 1 0 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, None, None, 6 12288 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, None, None, 6 76800 activation_6[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, None, None, 9 82944 activation_9[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, None, None, 3 6144 average_pooling2d[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, None, None, 6 192 conv2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, None, None, 6 192 conv2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, None, None, 9 288 conv2d_10[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, None, None, 3 96 conv2d_11[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, None, None, 6 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, None, None, 6 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, None, None, 9 0 batch_normalization_10[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, None, None, 3 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
mixed0 (Concatenate) (None, None, None, 2 0 activation_5[0][0]
activation_7[0][0]
activation_10[0][0]
activation_11[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, None, None, 6 16384 mixed0[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, None, None, 6 192 conv2d_15[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, None, None, 6 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, None, None, 4 12288 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, None, None, 9 55296 activation_15[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, None, None, 4 144 conv2d_13[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, None, None, 9 288 conv2d_16[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, None, None, 4 0 batch_normalization_13[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, None, None, 9 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, None, None, 2 0 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, None, None, 6 16384 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, None, None, 6 76800 activation_13[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, None, None, 9 82944 activation_16[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, None, None, 6 16384 average_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, None, None, 6 192 conv2d_12[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, None, None, 6 192 conv2d_14[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, None, None, 9 288 conv2d_17[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, None, None, 6 192 conv2d_18[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, None, None, 6 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, None, None, 6 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, None, None, 9 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, None, None, 6 0 batch_normalization_18[0][0]
__________________________________________________________________________________________________
mixed1 (Concatenate) (None, None, None, 2 0 activation_12[0][0]
activation_14[0][0]
activation_17[0][0]
activation_18[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, None, None, 6 18432 mixed1[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, None, None, 6 192 conv2d_22[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, None, None, 6 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, None, None, 4 13824 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, None, None, 9 55296 activation_22[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, None, None, 4 144 conv2d_20[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, None, None, 9 288 conv2d_23[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, None, None, 4 0 batch_normalization_20[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, None, None, 9 0 batch_normalization_23[0][0]
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, None, None, 2 0 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, None, None, 6 18432 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, None, None, 6 76800 activation_20[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, None, None, 9 82944 activation_23[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, None, None, 6 18432 average_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, None, None, 6 192 conv2d_19[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, None, None, 6 192 conv2d_21[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, None, None, 9 288 conv2d_24[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, None, None, 6 192 conv2d_25[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, None, None, 6 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, None, None, 6 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, None, None, 9 0 batch_normalization_24[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, None, None, 6 0 batch_normalization_25[0][0]
__________________________________________________________________________________________________
mixed2 (Concatenate) (None, None, None, 2 0 activation_19[0][0]
activation_21[0][0]
activation_24[0][0]
activation_25[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D) (None, None, None, 6 18432 mixed2[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, None, None, 6 192 conv2d_27[0][0]
__________________________________________________________________________________________________
activation_27 (Activation) (None, None, None, 6 0 batch_normalization_27[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D) (None, None, None, 9 55296 activation_27[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, None, None, 9 288 conv2d_28[0][0]
__________________________________________________________________________________________________
activation_28 (Activation) (None, None, None, 9 0 batch_normalization_28[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, None, None, 3 995328 mixed2[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D) (None, None, None, 9 82944 activation_28[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, None, None, 3 1152 conv2d_26[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, None, None, 9 288 conv2d_29[0][0]
__________________________________________________________________________________________________
activation_26 (Activation) (None, None, None, 3 0 batch_normalization_26[0][0]
__________________________________________________________________________________________________
activation_29 (Activation) (None, None, None, 9 0 batch_normalization_29[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, None, None, 2 0 mixed2[0][0]
__________________________________________________________________________________________________
mixed3 (Concatenate) (None, None, None, 7 0 activation_26[0][0]
activation_29[0][0]
max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D) (None, None, None, 1 98304 mixed3[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, None, None, 1 384 conv2d_34[0][0]
__________________________________________________________________________________________________
activation_34 (Activation) (None, None, None, 1 0 batch_normalization_34[0][0]
__________________________________________________________________________________________________
conv2d_35 (Conv2D) (None, None, None, 1 114688 activation_34[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, None, None, 1 384 conv2d_35[0][0]
__________________________________________________________________________________________________
activation_35 (Activation) (None, None, None, 1 0 batch_normalization_35[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D) (None, None, None, 1 98304 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D) (None, None, None, 1 114688 activation_35[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, None, None, 1 384 conv2d_31[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, None, None, 1 384 conv2d_36[0][0]
__________________________________________________________________________________________________
activation_31 (Activation) (None, None, None, 1 0 batch_normalization_31[0][0]
__________________________________________________________________________________________________
activation_36 (Activation) (None, None, None, 1 0 batch_normalization_36[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D) (None, None, None, 1 114688 activation_31[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D) (None, None, None, 1 114688 activation_36[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, None, None, 1 384 conv2d_32[0][0]
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, None, None, 1 384 conv2d_37[0][0]
__________________________________________________________________________________________________
activation_32 (Activation) (None, None, None, 1 0 batch_normalization_32[0][0]
__________________________________________________________________________________________________
activation_37 (Activation) (None, None, None, 1 0 batch_normalization_37[0][0]
__________________________________________________________________________________________________
average_pooling2d_3 (AveragePoo (None, None, None, 7 0 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D) (None, None, None, 1 147456 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D) (None, None, None, 1 172032 activation_32[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D) (None, None, None, 1 172032 activation_37[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D) (None, None, None, 1 147456 average_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, None, None, 1 576 conv2d_30[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, None, None, 1 576 conv2d_33[0][0]
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, None, None, 1 576 conv2d_38[0][0]
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, None, None, 1 576 conv2d_39[0][0]
__________________________________________________________________________________________________
activation_30 (Activation) (None, None, None, 1 0 batch_normalization_30[0][0]
__________________________________________________________________________________________________
activation_33 (Activation) (None, None, None, 1 0 batch_normalization_33[0][0]
__________________________________________________________________________________________________
activation_38 (Activation) (None, None, None, 1 0 batch_normalization_38[0][0]
__________________________________________________________________________________________________
activation_39 (Activation) (None, None, None, 1 0 batch_normalization_39[0][0]
__________________________________________________________________________________________________
mixed4 (Concatenate) (None, None, None, 7 0 activation_30[0][0]
activation_33[0][0]
activation_38[0][0]
activation_39[0][0]
__________________________________________________________________________________________________
conv2d_44 (Conv2D) (None, None, None, 1 122880 mixed4[0][0]
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, None, None, 1 480 conv2d_44[0][0]
__________________________________________________________________________________________________
activation_44 (Activation) (None, None, None, 1 0 batch_normalization_44[0][0]
__________________________________________________________________________________________________
conv2d_45 (Conv2D) (None, None, None, 1 179200 activation_44[0][0]
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, None, None, 1 480 conv2d_45[0][0]
__________________________________________________________________________________________________
activation_45 (Activation) (None, None, None, 1 0 batch_normalization_45[0][0]
__________________________________________________________________________________________________
conv2d_41 (Conv2D) (None, None, None, 1 122880 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_46 (Conv2D) (None, None, None, 1 179200 activation_45[0][0]
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, None, None, 1 480 conv2d_41[0][0]
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, None, None, 1 480 conv2d_46[0][0]
__________________________________________________________________________________________________
activation_41 (Activation) (None, None, None, 1 0 batch_normalization_41[0][0]
__________________________________________________________________________________________________
activation_46 (Activation) (None, None, None, 1 0 batch_normalization_46[0][0]
__________________________________________________________________________________________________
conv2d_42 (Conv2D) (None, None, None, 1 179200 activation_41[0][0]
__________________________________________________________________________________________________
conv2d_47 (Conv2D) (None, None, None, 1 179200 activation_46[0][0]
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, None, None, 1 480 conv2d_42[0][0]
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, None, None, 1 480 conv2d_47[0][0]
__________________________________________________________________________________________________
activation_42 (Activation) (None, None, None, 1 0 batch_normalization_42[0][0]
__________________________________________________________________________________________________
activation_47 (Activation) (None, None, None, 1 0 batch_normalization_47[0][0]
__________________________________________________________________________________________________
average_pooling2d_4 (AveragePoo (None, None, None, 7 0 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D) (None, None, None, 1 147456 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_43 (Conv2D) (None, None, None, 1 215040 activation_42[0][0]
__________________________________________________________________________________________________
conv2d_48 (Conv2D) (None, None, None, 1 215040 activation_47[0][0]
__________________________________________________________________________________________________
conv2d_49 (Conv2D) (None, None, None, 1 147456 average_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, None, None, 1 576 conv2d_40[0][0]
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, None, None, 1 576 conv2d_43[0][0]
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, None, None, 1 576 conv2d_48[0][0]
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, None, None, 1 576 conv2d_49[0][0]
__________________________________________________________________________________________________
activation_40 (Activation) (None, None, None, 1 0 batch_normalization_40[0][0]
__________________________________________________________________________________________________
activation_43 (Activation) (None, None, None, 1 0 batch_normalization_43[0][0]
__________________________________________________________________________________________________
activation_48 (Activation) (None, None, None, 1 0 batch_normalization_48[0][0]
__________________________________________________________________________________________________
activation_49 (Activation) (None, None, None, 1 0 batch_normalization_49[0][0]
__________________________________________________________________________________________________
mixed5 (Concatenate) (None, None, None, 7 0 activation_40[0][0]
activation_43[0][0]
activation_48[0][0]
activation_49[0][0]
__________________________________________________________________________________________________
conv2d_54 (Conv2D) (None, None, None, 1 122880 mixed5[0][0]
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, None, None, 1 480 conv2d_54[0][0]
__________________________________________________________________________________________________
activation_54 (Activation) (None, None, None, 1 0 batch_normalization_54[0][0]
__________________________________________________________________________________________________
conv2d_55 (Conv2D) (None, None, None, 1 179200 activation_54[0][0]
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, None, None, 1 480 conv2d_55[0][0]
__________________________________________________________________________________________________
activation_55 (Activation) (None, None, None, 1 0 batch_normalization_55[0][0]
__________________________________________________________________________________________________
conv2d_51 (Conv2D) (None, None, None, 1 122880 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_56 (Conv2D) (None, None, None, 1 179200 activation_55[0][0]
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, None, None, 1 480 conv2d_51[0][0]
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, None, None, 1 480 conv2d_56[0][0]
__________________________________________________________________________________________________
activation_51 (Activation) (None, None, None, 1 0 batch_normalization_51[0][0]
__________________________________________________________________________________________________
activation_56 (Activation) (None, None, None, 1 0 batch_normalization_56[0][0]
__________________________________________________________________________________________________
conv2d_52 (Conv2D) (None, None, None, 1 179200 activation_51[0][0]
__________________________________________________________________________________________________
conv2d_57 (Conv2D) (None, None, None, 1 179200 activation_56[0][0]
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, None, None, 1 480 conv2d_52[0][0]
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, None, None, 1 480 conv2d_57[0][0]
__________________________________________________________________________________________________
activation_52 (Activation) (None, None, None, 1 0 batch_normalization_52[0][0]
__________________________________________________________________________________________________
activation_57 (Activation) (None, None, None, 1 0 batch_normalization_57[0][0]
__________________________________________________________________________________________________
average_pooling2d_5 (AveragePoo (None, None, None, 7 0 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_50 (Conv2D) (None, None, None, 1 147456 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_53 (Conv2D) (None, None, None, 1 215040 activation_52[0][0]
__________________________________________________________________________________________________
conv2d_58 (Conv2D) (None, None, None, 1 215040 activation_57[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D) (None, None, None, 1 147456 average_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, None, None, 1 576 conv2d_50[0][0]
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, None, None, 1 576 conv2d_53[0][0]
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, None, None, 1 576 conv2d_58[0][0]
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, None, None, 1 576 conv2d_59[0][0]
__________________________________________________________________________________________________
activation_50 (Activation) (None, None, None, 1 0 batch_normalization_50[0][0]
__________________________________________________________________________________________________
activation_53 (Activation) (None, None, None, 1 0 batch_normalization_53[0][0]
__________________________________________________________________________________________________
activation_58 (Activation) (None, None, None, 1 0 batch_normalization_58[0][0]
__________________________________________________________________________________________________
activation_59 (Activation) (None, None, None, 1 0 batch_normalization_59[0][0]
__________________________________________________________________________________________________
mixed6 (Concatenate) (None, None, None, 7 0 activation_50[0][0]
activation_53[0][0]
activation_58[0][0]
activation_59[0][0]
__________________________________________________________________________________________________
conv2d_64 (Conv2D) (None, None, None, 1 147456 mixed6[0][0]
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, None, None, 1 576 conv2d_64[0][0]
__________________________________________________________________________________________________
activation_64 (Activation) (None, None, None, 1 0 batch_normalization_64[0][0]
__________________________________________________________________________________________________
conv2d_65 (Conv2D) (None, None, None, 1 258048 activation_64[0][0]
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, None, None, 1 576 conv2d_65[0][0]
__________________________________________________________________________________________________
activation_65 (Activation) (None, None, None, 1 0 batch_normalization_65[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D) (None, None, None, 1 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_66 (Conv2D) (None, None, None, 1 258048 activation_65[0][0]
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, None, None, 1 576 conv2d_61[0][0]
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, None, None, 1 576 conv2d_66[0][0]
__________________________________________________________________________________________________
activation_61 (Activation) (None, None, None, 1 0 batch_normalization_61[0][0]
__________________________________________________________________________________________________
activation_66 (Activation) (None, None, None, 1 0 batch_normalization_66[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D) (None, None, None, 1 258048 activation_61[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D) (None, None, None, 1 258048 activation_66[0][0]
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, None, None, 1 576 conv2d_62[0][0]
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, None, None, 1 576 conv2d_67[0][0]
__________________________________________________________________________________________________
activation_62 (Activation) (None, None, None, 1 0 batch_normalization_62[0][0]
__________________________________________________________________________________________________
activation_67 (Activation) (None, None, None, 1 0 batch_normalization_67[0][0]
__________________________________________________________________________________________________
average_pooling2d_6 (AveragePoo (None, None, None, 7 0 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D) (None, None, None, 1 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D) (None, None, None, 1 258048 activation_62[0][0]
__________________________________________________________________________________________________
conv2d_68 (Conv2D) (None, None, None, 1 258048 activation_67[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D) (None, None, None, 1 147456 average_pooling2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, None, None, 1 576 conv2d_60[0][0]
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, None, None, 1 576 conv2d_63[0][0]
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, None, None, 1 576 conv2d_68[0][0]
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, None, None, 1 576 conv2d_69[0][0]
__________________________________________________________________________________________________
activation_60 (Activation) (None, None, None, 1 0 batch_normalization_60[0][0]
__________________________________________________________________________________________________
activation_63 (Activation) (None, None, None, 1 0 batch_normalization_63[0][0]
__________________________________________________________________________________________________
activation_68 (Activation) (None, None, None, 1 0 batch_normalization_68[0][0]
__________________________________________________________________________________________________
activation_69 (Activation) (None, None, None, 1 0 batch_normalization_69[0][0]
__________________________________________________________________________________________________
mixed7 (Concatenate) (None, None, None, 7 0 activation_60[0][0]
activation_63[0][0]
activation_68[0][0]
activation_69[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D) (None, None, None, 1 147456 mixed7[0][0]
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, None, None, 1 576 conv2d_72[0][0]
__________________________________________________________________________________________________
activation_72 (Activation) (None, None, None, 1 0 batch_normalization_72[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D) (None, None, None, 1 258048 activation_72[0][0]
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, None, None, 1 576 conv2d_73[0][0]
__________________________________________________________________________________________________
activation_73 (Activation) (None, None, None, 1 0 batch_normalization_73[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D) (None, None, None, 1 147456 mixed7[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D) (None, None, None, 1 258048 activation_73[0][0]
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, None, None, 1 576 conv2d_70[0][0]
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, None, None, 1 576 conv2d_74[0][0]
__________________________________________________________________________________________________
activation_70 (Activation) (None, None, None, 1 0 batch_normalization_70[0][0]
__________________________________________________________________________________________________
activation_74 (Activation) (None, None, None, 1 0 batch_normalization_74[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D) (None, None, None, 3 552960 activation_70[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D) (None, None, None, 1 331776 activation_74[0][0]
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, None, None, 3 960 conv2d_71[0][0]
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, None, None, 1 576 conv2d_75[0][0]
__________________________________________________________________________________________________
activation_71 (Activation) (None, None, None, 3 0 batch_normalization_71[0][0]
__________________________________________________________________________________________________
activation_75 (Activation) (None, None, None, 1 0 batch_normalization_75[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, None, None, 7 0 mixed7[0][0]
__________________________________________________________________________________________________
mixed8 (Concatenate) (None, None, None, 1 0 activation_71[0][0]
activation_75[0][0]
max_pooling2d_3[0][0]
__________________________________________________________________________________________________
conv2d_80 (Conv2D) (None, None, None, 4 573440 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, None, None, 4 1344 conv2d_80[0][0]
__________________________________________________________________________________________________
activation_80 (Activation) (None, None, None, 4 0 batch_normalization_80[0][0]
__________________________________________________________________________________________________
conv2d_77 (Conv2D) (None, None, None, 3 491520 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_81 (Conv2D) (None, None, None, 3 1548288 activation_80[0][0]
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, None, None, 3 1152 conv2d_77[0][0]
__________________________________________________________________________________________________
batch_normalization_81 (BatchNo (None, None, None, 3 1152 conv2d_81[0][0]
__________________________________________________________________________________________________
activation_77 (Activation) (None, None, None, 3 0 batch_normalization_77[0][0]
__________________________________________________________________________________________________
activation_81 (Activation) (None, None, None, 3 0 batch_normalization_81[0][0]
__________________________________________________________________________________________________
conv2d_78 (Conv2D) (None, None, None, 3 442368 activation_77[0][0]
__________________________________________________________________________________________________
conv2d_79 (Conv2D) (None, None, None, 3 442368 activation_77[0][0]
__________________________________________________________________________________________________
conv2d_82 (Conv2D) (None, None, None, 3 442368 activation_81[0][0]
__________________________________________________________________________________________________
conv2d_83 (Conv2D) (None, None, None, 3 442368 activation_81[0][0]
__________________________________________________________________________________________________
average_pooling2d_7 (AveragePoo (None, None, None, 1 0 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_76 (Conv2D) (None, None, None, 3 409600 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, None, None, 3 1152 conv2d_78[0][0]
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, None, None, 3 1152 conv2d_79[0][0]
__________________________________________________________________________________________________
batch_normalization_82 (BatchNo (None, None, None, 3 1152 conv2d_82[0][0]
__________________________________________________________________________________________________
batch_normalization_83 (BatchNo (None, None, None, 3 1152 conv2d_83[0][0]
__________________________________________________________________________________________________
conv2d_84 (Conv2D) (None, None, None, 1 245760 average_pooling2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, None, None, 3 960 conv2d_76[0][0]
__________________________________________________________________________________________________
activation_78 (Activation) (None, None, None, 3 0 batch_normalization_78[0][0]
__________________________________________________________________________________________________
activation_79 (Activation) (None, None, None, 3 0 batch_normalization_79[0][0]
__________________________________________________________________________________________________
activation_82 (Activation) (None, None, None, 3 0 batch_normalization_82[0][0]
__________________________________________________________________________________________________
activation_83 (Activation) (None, None, None, 3 0 batch_normalization_83[0][0]
__________________________________________________________________________________________________
batch_normalization_84 (BatchNo (None, None, None, 1 576 conv2d_84[0][0]
__________________________________________________________________________________________________
activation_76 (Activation) (None, None, None, 3 0 batch_normalization_76[0][0]
__________________________________________________________________________________________________
mixed9_0 (Concatenate) (None, None, None, 7 0 activation_78[0][0]
activation_79[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, None, None, 7 0 activation_82[0][0]
activation_83[0][0]
__________________________________________________________________________________________________
activation_84 (Activation) (None, None, None, 1 0 batch_normalization_84[0][0]
__________________________________________________________________________________________________
mixed9 (Concatenate) (None, None, None, 2 0 activation_76[0][0]
mixed9_0[0][0]
concatenate[0][0]
activation_84[0][0]
__________________________________________________________________________________________________
conv2d_89 (Conv2D) (None, None, None, 4 917504 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_89 (BatchNo (None, None, None, 4 1344 conv2d_89[0][0]
__________________________________________________________________________________________________
activation_89 (Activation) (None, None, None, 4 0 batch_normalization_89[0][0]
__________________________________________________________________________________________________
conv2d_86 (Conv2D) (None, None, None, 3 786432 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_90 (Conv2D) (None, None, None, 3 1548288 activation_89[0][0]
__________________________________________________________________________________________________
batch_normalization_86 (BatchNo (None, None, None, 3 1152 conv2d_86[0][0]
__________________________________________________________________________________________________
batch_normalization_90 (BatchNo (None, None, None, 3 1152 conv2d_90[0][0]
__________________________________________________________________________________________________
activation_86 (Activation) (None, None, None, 3 0 batch_normalization_86[0][0]
__________________________________________________________________________________________________
activation_90 (Activation) (None, None, None, 3 0 batch_normalization_90[0][0]
__________________________________________________________________________________________________
conv2d_87 (Conv2D) (None, None, None, 3 442368 activation_86[0][0]
__________________________________________________________________________________________________
conv2d_88 (Conv2D) (None, None, None, 3 442368 activation_86[0][0]
__________________________________________________________________________________________________
conv2d_91 (Conv2D) (None, None, None, 3 442368 activation_90[0][0]
__________________________________________________________________________________________________
conv2d_92 (Conv2D) (None, None, None, 3 442368 activation_90[0][0]
__________________________________________________________________________________________________
average_pooling2d_8 (AveragePoo (None, None, None, 2 0 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_85 (Conv2D) (None, None, None, 3 655360 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_87 (BatchNo (None, None, None, 3 1152 conv2d_87[0][0]
__________________________________________________________________________________________________
batch_normalization_88 (BatchNo (None, None, None, 3 1152 conv2d_88[0][0]
__________________________________________________________________________________________________
batch_normalization_91 (BatchNo (None, None, None, 3 1152 conv2d_91[0][0]
__________________________________________________________________________________________________
batch_normalization_92 (BatchNo (None, None, None, 3 1152 conv2d_92[0][0]
__________________________________________________________________________________________________
conv2d_93 (Conv2D) (None, None, None, 1 393216 average_pooling2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_85 (BatchNo (None, None, None, 3 960 conv2d_85[0][0]
__________________________________________________________________________________________________
activation_87 (Activation) (None, None, None, 3 0 batch_normalization_87[0][0]
__________________________________________________________________________________________________
activation_88 (Activation) (None, None, None, 3 0 batch_normalization_88[0][0]
__________________________________________________________________________________________________
activation_91 (Activation) (None, None, None, 3 0 batch_normalization_91[0][0]
__________________________________________________________________________________________________
activation_92 (Activation) (None, None, None, 3 0 batch_normalization_92[0][0]
__________________________________________________________________________________________________
batch_normalization_93 (BatchNo (None, None, None, 1 576 conv2d_93[0][0]
__________________________________________________________________________________________________
activation_85 (Activation) (None, None, None, 3 0 batch_normalization_85[0][0]
__________________________________________________________________________________________________
mixed9_1 (Concatenate) (None, None, None, 7 0 activation_87[0][0]
activation_88[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, None, None, 7 0 activation_91[0][0]
activation_92[0][0]
__________________________________________________________________________________________________
activation_93 (Activation) (None, None, None, 1 0 batch_normalization_93[0][0]
__________________________________________________________________________________________________
mixed10 (Concatenate) (None, None, None, 2 0 activation_85[0][0]
mixed9_1[0][0]
concatenate_1[0][0]
activation_93[0][0]
==================================================================================================
Total params: 21,802,784
Trainable params: 21,768,352
Non-trainable params: 34,432
__________________________________________________________________________________________________
# Remove softmax layer(used for classification) and get the image vector.
# extract features from each image in the dataset
image_features_dict={}
for image, image_path in tqdm(New_Image_Data): #using tqdm as progress bar
features_for_batch = image_features_extract_model(image) #feeding images from above created dataset to Inception v3 which we build above
features_for_batch_flattened = tf.reshape(features_for_batch, (features_for_batch.shape[0], -1, features_for_batch.shape[3]))
for batch_feat, path in zip(features_for_batch_flattened, image_path):
feature_path = path.numpy().decode("utf-8")
image_features_dict[feature_path] = batch_feat.numpy()
100%|████████████████████████████████████████████████████████████████████████████████| 127/127 [07:56<00:00, 3.75s/it]
features_for_batch
<tf.Tensor: shape=(27, 8, 8, 2048), dtype=float32, numpy=
array([[[[0.00000000e+00, 7.74665326e-02, 0.00000000e+00, ...,
2.31837019e-01, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
1.07787609e-01, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 1.94032162e-01, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 1.85755104e-01],
...,
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
9.42666084e-02, 0.00000000e+00, 5.05790770e-01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],
[[0.00000000e+00, 4.23005015e-01, 1.34471357e-02, ...,
2.33669803e-01, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
4.99780476e-02, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 4.31933552e-02, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 2.55216211e-01],
...,
[0.00000000e+00, 0.00000000e+00, 5.62451780e-02, ...,
1.05212197e-01, 0.00000000e+00, 4.74512935e-01],
[0.00000000e+00, 4.84727323e-02, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 2.08402704e-03],
[0.00000000e+00, 6.13848716e-02, 0.00000000e+00, ...,
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features_for_batch_flattened
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0. ],
[0. , 0. , 0. , ..., 0.24869055, 0. ,
0. ],
[0. , 0. , 0. , ..., 0.5650912 , 0. ,
0. ],
...,
[0. , 0. , 0.76617575, ..., 0.28200567, 0.09333514,
0. ],
[0. , 0. , 0.8059678 , ..., 0.21559066, 0.75665665,
0.3692449 ],
[0. , 0. , 0. , ..., 0.40504813, 1.0184278 ,
0.7695485 ]], dtype=float32),
'Flickr8K/Images/2097398349_ff178b3f1b.jpg': array([[1.0941814 , 0. , 0.625846 , ..., 0. , 0. ,
0. ],
[0. , 0. , 0. , ..., 0.0888609 , 0. ,
0. ],
[0. , 0. , 0. , ..., 0.14122142, 0. ,
0. ],
...,
[0. , 0. , 0. , ..., 0.12275353, 0. ,
0. ],
[0. , 0. , 0. , ..., 0. , 0. ,
0. ],
[0. , 0.20615563, 0. , ..., 0.03335429, 0. ,
0. ]], dtype=float32),
'Flickr8K/Images/2097403787_77a154f5b9.jpg': array([[0. , 0. , 0. , ..., 1.5382324 , 0. ,
0. ],
[0.6410425 , 0. , 0. , ..., 1.606177 , 0. ,
0.07519861],
[0. , 0. , 0. , ..., 1.6300031 , 0. ,
0.70758975],
...,
[0. , 0. , 0.6360018 , ..., 0.72154367, 1.0211551 ,
0. ],
[0.11510214, 0. , 0.87667656, ..., 0.04932512, 0.25569308,
0. ],
[0.5156163 , 0. , 1.2933018 , ..., 0. , 0. ,
0. ]], dtype=float32),
'Flickr8K/Images/2097407245_c798e0dcaf.jpg': array([[0. , 0. , 0.39558068, ..., 0. , 1.6241463 ,
0.83743125],
[0.44887337, 0. , 0.01561378, ..., 0. , 1.4416461 ,
0.5191072 ],
[0.3475302 , 0. , 0. , ..., 0.38411945, 1.3270442 ,
0.92452127],
...,
[0. , 0.52947164, 0. , ..., 0. , 0.37491316,
0. ],
[0. , 0. , 0.18878296, ..., 0.00369347, 0.07988718,
0. ],
[0. , 0. , 1.5026851 , ..., 0.3800797 , 0. ,
0. ]], dtype=float32),
'Flickr8K/Images/2097420505_439f63c863.jpg': array([[0. , 0. , 0.46559086, ..., 1.0621407 , 0. ,
0.278887 ],
[0. , 0.11765599, 0. , ..., 1.0795671 , 0. ,
0.41522238],
[0. , 0. , 0. , ..., 1.1625496 , 0. ,
0.25449792],
...,
[0.00770566, 0.55277634, 0.13271114, ..., 0.44901443, 0. ,
0. ],
[0. , 0. , 0.6933311 , ..., 1.121058 , 0. ,
0. ],
[0. , 0. , 1.2095078 , ..., 1.328044 , 0. ,
0. ]], dtype=float32),
'Flickr8K/Images/2097489021_ca1b9f5c3b.jpg': array([[0.15785247, 0. , 0. , ..., 0. , 0.50187457,
0. ],
[0. , 1.48975 , 0. , ..., 0. , 0.8662035 ,
0. ],
[0. , 1.4401306 , 0.5894115 , ..., 0.18876597, 1.1296148 ,
0.39333606],
...,
[0.09279944, 1.0721037 , 0.7764622 , ..., 0.7474727 , 0. ,
0.03147933],
[0. , 0.2504396 , 0.44869187, ..., 0. , 0. ,
0. ],
[0. , 0. , 0. , ..., 0. , 0. ,
0. ]], dtype=float32),
'Flickr8K/Images/2098174172_e57d86ea03.jpg': array([[0. , 0. , 0.3579944 , ..., 0.65740526, 0. ,
0. ],
[0.28626966, 0. , 0. , ..., 0.9620331 , 0. ,
0. ],
[0. , 0. , 0. , ..., 1.1881698 , 0. ,
0.09020985],
...,
[0. , 0. , 0. , ..., 0.7708007 , 0. ,
0. ],
[0.16500708, 0. , 0. , ..., 0.36161914, 0. ,
0. ],
[1.1403291 , 0. , 0. , ..., 0.09770657, 0. ,
0. ]], dtype=float32),
'Flickr8K/Images/2098418613_85a0c9afea.jpg': array([[0. , 0. , 0. , ..., 0. , 0. ,
0. ],
[0. , 0. , 0. , ..., 0. , 0. ,
0. ],
[0. , 0. , 0. , ..., 0. , 0. ,
0. ],
...,
[0. , 0. , 0.7405528 , ..., 0. , 0.6391579 ,
0. ],
[0.39201608, 0. , 0.8800453 , ..., 0. , 0.1263821 ,
0. ],
[0. , 0. , 0.92864585, ..., 0. , 0.01319708,
0. ]], dtype=float32),
...}
batch_feat.shape
TensorShape([64, 2048])
top_5_items = more_itertools.take(5, image_features_dict.items()) #return first n items of the iterable as a list
top_5_items #printing first 5 elememts have dictionary
[('Flickr8K/Images/1000268201_693b08cb0e.jpg',
array([[0. , 0. , 0. , ..., 0. , 0. ,
0. ],
[0. , 0. , 0. , ..., 0. , 0. ,
0.16987158],
[0. , 0. , 1.0105941 , ..., 0. , 0. ,
0.06111852],
...,
[0. , 0. , 2.038044 , ..., 1.2029713 , 0. ,
1.9924142 ],
[0. , 0. , 0.90469027, ..., 0. , 0. ,
0. ],
[0. , 1.2527303 , 0. , ..., 0. , 0. ,
0. ]], dtype=float32)),
('Flickr8K/Images/1001773457_577c3a7d70.jpg',
array([[0. , 0.66368175, 0.46285966, ..., 0. , 0. ,
3.3322608 ],
[0. , 1.2268165 , 0. , ..., 0. , 0.253902 ,
3.3295352 ],
[1.2881926 , 1.519541 , 0. , ..., 0.4677789 , 0.36474568,
3.8172603 ],
...,
[0.33720088, 2.877539 , 0. , ..., 0. , 1.0885158 ,
0. ],
[0.20815182, 1.0365013 , 0. , ..., 0. , 0.57464945,
0. ],
[0. , 0.5454986 , 0. , ..., 0. , 0.27576348,
0. ]], dtype=float32)),
('Flickr8K/Images/1002674143_1b742ab4b8.jpg',
array([[0. , 0. , 0. , ..., 1.3794606 , 0.2304443 ,
0. ],
[0. , 0. , 0. , ..., 0.8410628 , 0.1040877 ,
0. ],
[0.0046417 , 0. , 0. , ..., 0. , 0.08157845,
0. ],
...,
[0. , 0. , 0. , ..., 1.0989287 , 0. ,
0. ],
[0. , 0. , 0.21060869, ..., 1.2415987 , 0.25440937,
0. ],
[0. , 0. , 0. , ..., 1.403109 , 0.9622641 ,
0. ]], dtype=float32)),
('Flickr8K/Images/1003163366_44323f5815.jpg',
array([[0. , 0. , 0. , ..., 0. , 0. ,
0. ],
[0. , 0. , 0. , ..., 0. , 0. ,
0. ],
[0. , 0. , 0. , ..., 0. , 0. ,
0. ],
...,
[0. , 0. , 0. , ..., 0.00737922, 0. ,
0.3763362 ],
[0. , 0. , 0. , ..., 0. , 0. ,
0.60719395],
[1.1144838 , 1.220762 , 0. , ..., 0.06530861, 0. ,
0.81927 ]], dtype=float32)),
('Flickr8K/Images/1007129816_e794419615.jpg',
array([[3.5013187e-01, 4.7103885e-01, 0.0000000e+00, ..., 2.3987542e-01,
4.1224435e-04, 4.5422769e-01],
[0.0000000e+00, 6.8102109e-01, 0.0000000e+00, ..., 0.0000000e+00,
0.0000000e+00, 2.0593593e-01],
[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00,
0.0000000e+00, 0.0000000e+00],
...,
[5.2238965e-01, 0.0000000e+00, 0.0000000e+00, ..., 2.5506815e-01,
0.0000000e+00, 0.0000000e+00],
[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00,
0.0000000e+00, 0.0000000e+00],
[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00,
0.0000000e+00, 0.0000000e+00]], dtype=float32))]
#model i/p: feature, caption
def model_input(image,captions):
image_final = image_features_dict[image.decode('utf-8')] #feature of image from dict
return image_final,captions
#batching and shuffling
#always shuffle before batching
#tensorflow could not shuffle the whole dataset so we make it shuffle buffer sizes
BUFFER_SIZE = 1000
#Batch size - should be 2**x for good measure
BATCH_SIZE = 128
def data_generator(images_data, captions_data):
#Create a Dataset whose elements are slices of the given tensors
dataset = tf.data.Dataset.from_tensor_slices((images_data, captions_data))
#the number of parallel calls is set dynamically based on available CPU, saves time
dataset = dataset.map(lambda item1, item2: tf.numpy_function(model_input, [item1, item2], [tf.float32, tf.int32]),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
#reshuffle_each_iteration=True indicate Shuffle order should be different for each epoch.
#Keeping batch_size=64 (64 consecutive elements of this dataset is combined in a single batch).
#We don't want to drop the last batch if it contains element less than 64.So let's set drop_remainder=False
#Prefetch function is used to prepare later elements while the current element is being processed.
dataset = (dataset.shuffle(BUFFER_SIZE, reshuffle_each_iteration=True).batch(BATCH_SIZE, drop_remainder=False).prefetch(tf.data.experimental.AUTOTUNE))
return dataset
train_dataset=data_generator(img_train_data,caption_train_data)
test_dataset=data_generator(img_test_data,caption_test_data)
sample_img_batch, sample_cap_batch = next(iter(train_dataset))
print(sample_img_batch.shape) #(batch_size, 8*8, 2048)
print(sample_cap_batch.shape) #(batch_size, 31)
(128, 64, 2048) (128, 31)
Model Building:
embedding_dim = 256
units = 512
vocab_size = 5001 #top 5,000 words +1
train_num_steps = len(img_train_data) // BATCH_SIZE
test_num_steps = len(img_test_data) // BATCH_SIZE
max_length = 31
features_shape = batch_feat.shape[1] #64
attention_features_shape = batch_feat.shape[0] #2048
Encoder
#Clears the default graph stack and resets the global default graph - to resolve compatibility issue
tf.compat.v1.reset_default_graph()
print(tf.compat.v1.get_default_graph())
<tensorflow.python.framework.ops.Graph object at 0x000002E055BE5EB8>
class Encoder(Model):
def __init__(self, embed_dim):
super(Encoder, self).__init__()
self.dense = tf.keras.layers.Dense(embed_dim) #build your Dense layer with relu activation
#It acts as the fully connected layer
def call(self, features):
features = self.dense(features) # extract the features from the image shape: (batch, 8*8, embed_dim)
features = tf.keras.activations.relu(features, alpha=0.01, max_value=None, threshold=0) #applying relu activation
#The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero
#alpha -> governs the slope for values lower than the threshold - use a non-zero multiple of the input for values below the threshold
#max_value -> sets the the largest value the function will return
#threshold -> the threshold value of the activation function below which values will be damped or set to zero.
return features
encoder=Encoder(embedding_dim)
from keras.utils.vis_utils import plot_model
plot_model(encoder, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
Attention model
class Attention_model(Model):
def __init__(self, units):
super(Attention_model, self).__init__()
self.W1 = tf.keras.layers.Dense(units)#Dense layer
self.W2 = tf.keras.layers.Dense(units) #Dense layer
self.V = tf.keras.layers.Dense(1)#final Dense layer with unit 1
self.units=units
def call(self, features, hidden):
#features shape: (batch_size, 8*8, embedding_dim)
#hidden shape: (batch_size, hidden_size)
hidden_with_time_axis=hidden[:, tf.newaxis] # Expand the hidden shape to shape: (batch_size, 1, hidden_size)
score =tf.keras.activations.tanh(self.W1(features) + self.W2(hidden_with_time_axis))# build your score function to shape: (batch_size, 8*8, units)
attention_weights = tf.keras.activations.softmax(self.V(score), axis=1)# extract your attention weights with shape: (batch_size, 8*8, 1)
context_vector = attention_weights * features #shape: create the context vector with shape (batch_size, 8*8,embedding_dim)
context_vector = tf.reduce_sum(context_vector, axis=1)# reduce the shape to (batch_size, embedding_dim)
# the tensor is reduced along the second dimension (columns)
return context_vector, attention_weights
Decoder
class Decoder(Model):
def __init__(self, embed_dim, units, vocab_size):
super(Decoder, self).__init__()
self.units=units
self.attention = Attention_model(self.units) #iniitalise your Attention model with units
self.embed = tf.keras.layers.Embedding(vocab_size, embed_dim) #build your Embedding layer - Turns positive integers (indexes) into dense vectors of fixed size
self.gru = tf.keras.layers.GRU(self.units,return_sequences=True,return_state=True,recurrent_initializer='glorot_uniform') #Gated Recurrent Unit
#units - dimensionality of the output space
#return_sequences - not the last output in the output sequence, return the full sequence
#return_state - return the last state in addition to the output
#recurrent_initializer - used for the linear transformation of the recurrent state. Default: orthogonal
#Draws samples from a uniform distribution within [-limit, limit]
#limit = sqrt(6 / (fan_in + fan_out)) (fan_in is the number of input units in the weight tensor and fan_out is the number of output units)
self.d1 = tf.keras.layers.Dense(self.units) #build your Dense layer
self.d2 = tf.keras.layers.Dense(vocab_size) #build your Dense layer
def call(self,x,features, hidden):
context_vector, attention_weights = self.attention(features, hidden) #create your context vector & attention weights from attention model
embed = self.embed(x) #embed input to shape: (batch_size, 1, embedding_dim)
embed = tf.concat([tf.expand_dims(context_vector, 1), embed], axis=-1) #Concatenate your input with the context vector from attention layer. Shape: (batch_size, 1, embedding_dim + embedding_dim)
output,state = self.gru(embed) #Extract the output & hidden state from GRU layer. Output shape : (batch_size, max_length, hidden_size)
output = self.d1(output)
output = tf.reshape(output, (-1, output.shape[2])) # shape : (batch_size * max_length, hidden_size) -1 => remove the dimension
output = self.d2(output) # shape : (batch_size * max_length, vocab_size)
return output, state, attention_weights
def init_state(self, batch_size):
return tf.zeros((batch_size, self.units))
Final output: vocab_size because we need to assign probability for every word in vocabulary
decoder=Decoder(embedding_dim, units, vocab_size)
features = encoder(sample_img_batch)
hidden = decoder.init_state(batch_size=sample_cap_batch.shape[0])
dec_input = tf.expand_dims([tokenizer.word_index['<start>']] * sample_cap_batch.shape[0], 1)
predictions, hidden_out, attention_weights= decoder(dec_input, features, hidden)
print('Feature shape from Encoder: {}'.format(features.shape)) #(batch, 8*8, embed_dim)
print('Predictions shape from Decoder: {}'.format(predictions.shape)) #(batch,vocab_size)
print('Attention weights shape from Decoder: {}'.format(attention_weights.shape)) #(batch, 8*8, embed_dim)
Feature shape from Encoder: (128, 64, 256) Predictions shape from Decoder: (128, 5001) Attention weights shape from Decoder: (128, 64, 1)
Model training & optimization:
#Step 1:
#Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments, alternative to gradient descent
optimizer =tf.keras.optimizers.Adam(learning_rate=0.001) #define the optimizer
#Computes the crossentropy loss between the labels and predictions.
loss_object =tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE) #define your loss object
#we always need to choose appropriate loss function.Let us use crossentropy SparseCategoricalCrossentropy.
# It calculates loss between the labels and predictions.
#the prev introduced 0s need to be masked because we dont want decoder to give importance to zero
def loss_function(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0)) #Returns the truth value of NOT x element-wise
loss_ = loss_object(real, pred) #labels, predictions
mask = tf.cast(mask, dtype=loss_.dtype) #Casts a tensor to a new type
loss_ *= mask # we are multiplying loss with mask to get ideal shape
return tf.reduce_mean(loss_) #Computes the mean of elements across dimensions of a tensor
#step 2:
checkpoint_path = "Flickr8K/checkpoint1"
ckpt = tf.train.Checkpoint(encoder=encoder,
decoder=decoder,
optimizer = optimizer)
ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)
start_epoch = 0
if ckpt_manager.latest_checkpoint: #Find the filename of latest saved checkpoint file
start_epoch = int(ckpt_manager.latest_checkpoint.split('-')[-1])
#Compiles a function into a callable TensorFlow graph
@tf.function
def train_step(img_tensor, target):
loss = 0
hidden = decoder.init_state(batch_size=target.shape[0])
#Returns a tensor with a length 1 axis inserted at index axis(here 1)
dec_input = tf.expand_dims([tokenizer.word_index['<start>']] * target.shape[0], 1)
#Record operations for automatic differentiation for implementing backpropagation - calculates the gradient of the error function with respect to the neural network's weights
with tf.GradientTape() as tape: #Record operations for automatic differentiation
encoder_output = encoder(img_tensor)
# Using the teacher forcing technique where the target word is passed as the next input to the decoder
for t in range(1, target.shape[1]):
# passing encoder_output to the decoder
predictions, hidden, _ = decoder(dec_input, encoder_output, hidden)
loss += loss_function(target[:, t], predictions) #loss for batch
dec_input = tf.expand_dims(target[:, t], 1) #decoder input
avg_loss = (loss / int(target.shape[1])) #we are calculating average loss for every batch
tot_trainables_variables = encoder.trainable_variables + decoder.trainable_variables #Returns all variables created with trainable=True.
grads = tape.gradient(loss, tot_trainables_variables) # to calculate gradients with respect to every trainable variable
#compute gradients and apply it to the optimizer and backpropagate.
optimizer.apply_gradients(zip(grads, tot_trainables_variables))
return loss, avg_loss
#same but for testing
@tf.function
def test_step(img_tensor, target):
loss = 0
hidden = decoder.init_state(batch_size=target.shape[0])
dec_input = tf.expand_dims([tokenizer.word_index['<start>']] * target.shape[0], 1)
with tf.GradientTape() as tape: #Record operations for automatic differentiation.
encoder_output = encoder(img_tensor)
for t in range(1, target.shape[1]):
predictions, hidden, _ = decoder(dec_input, encoder_output, hidden)
loss += loss_function(target[:, t], predictions)
# using teacher forcing - target word is passed as the next input to the decoder
dec_input = tf.expand_dims(target[:, t], 1)
avg_loss = (loss / int(target.shape[1]))#we are calculating average loss for every batch
tot_trainables_variables = encoder.trainable_variables + decoder.trainable_variables
grads = tape.gradient(loss, tot_trainables_variables) # to calculate gradients with respect to every trainable variable
#compute gradients and apply it to the optimizer and backpropagate.
optimizer.apply_gradients(zip(grads, tot_trainables_variables))
return loss, avg_loss
#get loss on test data
def test_loss_cal(test_dataset):
total_loss = 0
for (batch, (img_tensor, target)) in enumerate(test_dataset):
batch_loss, t_loss = test_step(img_tensor, target)
total_loss += t_loss
avg_test_loss=total_loss / test_num_steps
return avg_test_loss
loss_plot = []
test_loss_plot = []
EPOCHS = 15
best_test_loss=100
for epoch in tqdm(range(0, EPOCHS)): #progress bar
start = time.time()
total_loss = 0
for (batch, (img_tensor, target)) in enumerate(train_dataset):
batch_loss, t_loss = train_step(img_tensor, target)
total_loss += t_loss
avg_train_loss=total_loss / train_num_steps
loss_plot.append(avg_train_loss)
test_loss = test_loss_cal(test_dataset)
test_loss_plot.append(test_loss)
print ('For epoch: {}, the train loss is {:.3f}, & test loss is {:.3f}'.format(epoch+1,avg_train_loss,test_loss))
print ('Time taken for 1 epoch {} sec\n'.format(time.time() - start))
if test_loss < best_test_loss:
print('Test loss has been reduced from %.3f to %.3f' % (best_test_loss, test_loss))
best_test_loss = test_loss
ckpt_manager.save()
7%|█████▎ | 1/15 [11:22<2:39:19, 682.79s/it]
For epoch: 1, the train loss is 1.560, & test loss is 1.288 Time taken for 1 epoch 682.6175239086151 sec Test loss has been reduced from 100.000 to 1.288
13%|██████████▋ | 2/15 [21:04<2:15:03, 623.36s/it]
For epoch: 2, the train loss is 1.175, & test loss is 1.123 Time taken for 1 epoch 581.5901782512665 sec Test loss has been reduced from 1.288 to 1.123
20%|████████████████ | 3/15 [32:42<2:11:26, 657.22s/it]
For epoch: 3, the train loss is 1.059, & test loss is 1.026 Time taken for 1 epoch 697.3503911495209 sec Test loss has been reduced from 1.123 to 1.026
27%|█████████████████████▎ | 4/15 [45:32<2:08:41, 701.99s/it]
For epoch: 4, the train loss is 0.982, & test loss is 0.960 Time taken for 1 epoch 770.4542305469513 sec Test loss has been reduced from 1.026 to 0.960
33%|██████████████████████████▋ | 5/15 [58:56<2:03:08, 738.83s/it]
For epoch: 5, the train loss is 0.921, & test loss is 0.902 Time taken for 1 epoch 803.9868686199188 sec Test loss has been reduced from 0.960 to 0.902
40%|███████████████████████████████▏ | 6/15 [1:12:26<1:54:27, 763.01s/it]
For epoch: 6, the train loss is 0.871, & test loss is 0.852 Time taken for 1 epoch 809.7707357406616 sec Test loss has been reduced from 0.902 to 0.852
47%|████████████████████████████████████▍ | 7/15 [1:25:44<1:43:15, 774.43s/it]
For epoch: 7, the train loss is 0.826, & test loss is 0.809 Time taken for 1 epoch 797.7512722015381 sec Test loss has been reduced from 0.852 to 0.809
53%|█████████████████████████████████████████▌ | 8/15 [1:39:07<1:31:24, 783.46s/it]
For epoch: 8, the train loss is 0.785, & test loss is 0.766 Time taken for 1 epoch 802.6320760250092 sec Test loss has been reduced from 0.809 to 0.766 For epoch: 9, the train loss is 0.746, & test loss is 0.731 Time taken for 1 epoch 830.4263999462128 sec Test loss has been reduced from 0.766 to 0.731
60%|██████████████████████████████████████████████▊ | 9/15 [1:52:58<1:19:49, 798.21s/it]
For epoch: 10, the train loss is 0.711, & test loss is 0.695 Time taken for 1 epoch 893.5444176197052 sec Test loss has been reduced from 0.731 to 0.695
73%|█████████████████████████████████████████████████████████▉ | 11/15 [2:21:54<55:28, 832.16s/it]
For epoch: 11, the train loss is 0.678, & test loss is 0.660 Time taken for 1 epoch 842.0906774997711 sec Test loss has been reduced from 0.695 to 0.660
80%|███████████████████████████████████████████████████████████████▏ | 12/15 [2:35:01<40:55, 818.41s/it]
For epoch: 12, the train loss is 0.647, & test loss is 0.634 Time taken for 1 epoch 786.8104968070984 sec Test loss has been reduced from 0.660 to 0.634
87%|████████████████████████████████████████████████████████████████████▍ | 13/15 [2:48:13<27:00, 810.48s/it]
For epoch: 13, the train loss is 0.617, & test loss is 0.601 Time taken for 1 epoch 792.0189068317413 sec Test loss has been reduced from 0.634 to 0.601
93%|█████████████████████████████████████████████████████████████████████████▋ | 14/15 [3:01:11<13:20, 800.75s/it]
For epoch: 14, the train loss is 0.590, & test loss is 0.581 Time taken for 1 epoch 778.1178154945374 sec Test loss has been reduced from 0.601 to 0.581
100%|███████████████████████████████████████████████████████████████████████████████| 15/15 [3:14:28<00:00, 777.88s/it]
For epoch: 15, the train loss is 0.567, & test loss is 0.556 Time taken for 1 epoch 796.3547427654266 sec Test loss has been reduced from 0.581 to 0.556
plt.plot(loss_plot)
plt.plot(test_loss_plot)
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.title('Loss Plot')
plt.grid('off')
plt.show()
Model Evaluation:
#greedy search
def evaluate(image):
attention_plot = np.zeros((max_length, attention_features_shape))
hidden = decoder.init_state(batch_size=1)
#process the input image to desired format before extracting features
temp_input = tf.expand_dims(preprocess_images_function(image)[0], 0)
# Extract features using our feature extraction model
img_tensor_val = image_features_extract_model(temp_input)
img_tensor_val = tf.reshape(img_tensor_val, (img_tensor_val.shape[0], -1, img_tensor_val.shape[3]))
# extract the features by passing the input to encoder
features = encoder(img_tensor_val)
#get value to send to decoder
dec_input = tf.expand_dims([tokenizer.word_index['<start>']], 0)
result = []
for i in range(max_length):
predictions, hidden, attention_weights = decoder(dec_input, features, hidden)# get the output from decoder
attention_plot[i] = tf.reshape(attention_weights, (-1, )).numpy()
#extract the predicted id(embedded value) which carries the max value
predicted_id = tf.argmax(predictions[0]).numpy()
#map the id to the word from tokenizer and append the value to the result list
result.append(tokenizer.index_word[predicted_id])
#end of a caption/"token"
if tokenizer.index_word[predicted_id] == '<end>':
return result, attention_plot,predictions
dec_input = tf.expand_dims([predicted_id], 0)
attention_plot = attention_plot[:len(result), :]
return result, attention_plot,predictions
def plot_attmap(caption, weights, image):
fig = plt.figure(figsize=(10, 10))
temp_img = np.array(Image.open(image))
len_cap = len(caption)
for cap in range(len_cap):
weights_img = np.reshape(weights[cap], (8,8))
weights_img = np.array(Image.fromarray(weights_img).resize((224, 224), Image.LANCZOS)) #for image scaling
ax = fig.add_subplot(len_cap//2, len_cap//2, cap+1)
ax.set_title(caption[cap], fontsize=15)
img=ax.imshow(temp_img)
#The Colormap instance or registered colormap name used to map scalar data to colors. This parameter is ignored for RGB(A) data.
ax.imshow(weights_img, cmap='gist_heat', alpha=0.6,extent=img.get_extent()) #here shades of read
ax.axis('off')
plt.subplots_adjust(hspace=0.2, wspace=0.2) # the amount of height and width reserved for space between subplots,
plt.show()
#filtering <start>, <end> and <unk> (because we conv to lowercase)
def filt_text(text):
filt=['<start>','<unk>','<end>']
temp= text.split()
[temp.remove(j) for k in filt for j in temp if k==j]
text=' '.join(temp)
return text
img_test=img_test_data.copy()
#modifying the given code as function to run it several times by changing weights
def predicted_caption_audio(random,autoplay=False,weights=(0.5, 0.5, 0, 0)):
cap_test=caption_test_data.copy()
rid = np.random.randint(0, random)
test_image = img_test[rid]
real_caption = ' '.join([tokenizer.index_word[i] for i in cap_test[rid] if i not in [0]])
result, attention_plot,pred_test = evaluate(test_image)
real_caption=filt_text(real_caption)
pred_caption=' '.join(result).rsplit(' ', 1)[0]
real_appn = []
real_appn.append(real_caption.split())
reference = real_appn
candidate = pred_caption.split()
score = sentence_bleu(reference, candidate, weights=weights)#set your weights)
print(f"BELU score: {score*100}")
print ('Real Caption:', real_caption)
print ('Prediction Caption:', pred_caption)
plot_attmap(result, attention_plot, test_image)
#Let's use Google Text to Speech Online API from playing the predicted caption as audio
speech = gTTS("Predicted Caption is: "+ pred_caption,lang = 'en', slow = False)
speech.save('voice.mp3')
audio_file = 'voice.mp3'
#playsound('voice.wav')
display.display(display.Audio(audio_file, rate=None,autoplay=autoplay))
return test_image
test_image=predicted_caption_audio(5,True,weights=(0.5,0.25,0,0))
Image.open(test_image)
C:\Users\subha\anaconda3\envs\hello-tf\lib\site-packages\nltk\translate\bleu_score.py:515: UserWarning: The hypothesis contains 0 counts of 4-gram overlaps. Therefore the BLEU score evaluates to 0, independently of how many N-gram overlaps of lower order it contains. Consider using lower n-gram order or use SmoothingFunction() warnings.warn(_msg)
BELU score: 44.77118844014734 Real Caption: large wild cat is pursuing horse across meadow Prediction Caption: the dirty dog is pursuing horse pursuing animal
#test_image=predicted_caption_audio(len(img_test),False,weights=(0.5,0.25,0,0))
#Image.open(test_image)
test_image1=predicted_caption_audio(len(img_test),False,weights=(0.25,0.25,0,0))
Image.open(test_image1)
C:\Users\subha\anaconda3\envs\hello-tf\lib\site-packages\nltk\translate\bleu_score.py:515: UserWarning: The hypothesis contains 0 counts of 2-gram overlaps. Therefore the BLEU score evaluates to 0, independently of how many N-gram overlaps of lower order it contains. Consider using lower n-gram order or use SmoothingFunction() warnings.warn(_msg)
BELU score: 2.446225477056264e-76 Real Caption: skateboarder performs skateboard trick against graffiti wall Prediction Caption: skateboarder grinding lip
test_image=predicted_caption_audio(len(img_test),False,weights=(0.5,0.25,0,0))
Image.open(test_image)
C:\Users\subha\anaconda3\envs\hello-tf\lib\site-packages\nltk\translate\bleu_score.py:515: UserWarning: The hypothesis contains 0 counts of 4-gram overlaps. Therefore the BLEU score evaluates to 0, independently of how many N-gram overlaps of lower order it contains. Consider using lower n-gram order or use SmoothingFunction() warnings.warn(_msg)
BELU score: 27.30248242133568 Real Caption: young man wearing green and white shirt hanging by two straps in subway car Prediction Caption: boy in green shirt playing in subway car
test_image=predicted_caption_audio(len(img_test),False,weights=(0.5,0.5,0,0))
Image.open(test_image)
BELU score: 0 Real Caption: smiling woman punching dog Prediction Caption: the two people are playing on the sidewalk
test_image=predicted_caption_audio(len(img_test),False,weights=(0.5,0.5,0,0))
Image.open(test_image)
BELU score: 0 Real Caption: couple are holding hands next to very large mirror outdoors Prediction Caption: man in red shirt and sunglasses on busy street
